diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index a907c4a2a1e2bb9162dd957ad61bd2d16dde8dd0..fc9a7a40641e8ee27323e91ca5a4ac7f6883a3fd 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -24,6 +24,8 @@ before_script:
tests:
stage: tests
+ services:
+ - redis
before_script:
- apt update
- apt install -y libgl1-mesa-glx xvfb graphviz libgraphviz-dev xdg-utils libcairo2-dev libjpeg-dev libgif-dev python-pyglet
@@ -102,6 +104,8 @@ benchmarks:
test_conda_setup:
stage: integration_testing
+ services:
+ - redis
before_script:
- apt update
- apt install -y libgl1-mesa-glx xvfb xdg-utils libcairo2-dev libjpeg-dev libgif-dev graphviz libgraphviz-dev python-pyglet
diff --git a/env_data/tests/Test_9_Level_1.pkl b/env_data/tests/Test_9_Level_1.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..e18a542f8a9d9caf2cca8046be420181ff91ce26
Binary files /dev/null and b/env_data/tests/Test_9_Level_1.pkl differ
diff --git a/env_data/tests/service_test/Test_0/Level_0.pkl b/env_data/tests/service_test/Test_0/Level_0.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..e18a542f8a9d9caf2cca8046be420181ff91ce26
Binary files /dev/null and b/env_data/tests/service_test/Test_0/Level_0.pkl differ
diff --git a/env_data/tests/service_test/Test_0/Level_1.pkl b/env_data/tests/service_test/Test_0/Level_1.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..e18a542f8a9d9caf2cca8046be420181ff91ce26
Binary files /dev/null and b/env_data/tests/service_test/Test_0/Level_1.pkl differ
diff --git a/env_data/tests/service_test/metadata.csv b/env_data/tests/service_test/metadata.csv
new file mode 100644
index 0000000000000000000000000000000000000000..021cae21e0c70cb176848aaed2662787e672f983
--- /dev/null
+++ b/env_data/tests/service_test/metadata.csv
@@ -0,0 +1,3 @@
+test_id,env_id,n_agents,x_dim,y_dim,n_cities,max_rails_in_city,malfunction_interval,n_envs_run,seed,grid_mode,max_rails_between_cities,malfunction_duration_min,malfunction_duration_max,speed_ratios
+Test_0,Level_0,5,25,25,2,3,50,50,11111,False,2,20,50,{1.0: 1.0}
+Test_0,Level_1,5,25,25,2,3,50,50,11111,False,2,20,50,{1.0: 1.0}
diff --git a/flatland/__init__.py b/flatland/__init__.py
index dacefcb29931607d2c6b718ad83206f5091be23d..9444a28625957f4089ff257e902e348ed74afa8c 100644
--- a/flatland/__init__.py
+++ b/flatland/__init__.py
@@ -4,4 +4,4 @@
__author__ = """S.P. Mohanty"""
__email__ = 'mohanty@aicrowd.com'
-__version__ = '2.2.1'
+__version__ = '2.2.2'
diff --git a/flatland/cli.py b/flatland/cli.py
index 51db57b3bc5c7e2889ac57df84c95a15b59685f7..2bd5cca2730772592f6a345d3328f8c6ae1d1df8 100644
--- a/flatland/cli.py
+++ b/flatland/cli.py
@@ -63,7 +63,7 @@ def demo(args=None):
)
@click.option('--shuffle',
type=bool,
- default=True,
+ default=False,
help="Shuffle the environments before starting evaluation.",
required=False
)
diff --git a/flatland/envs/agent_chains.py b/flatland/envs/agent_chains.py
index a2792479c0877aef2b41a8b62af7d55f5ab46bb0..3e566ad0617a3c49ec69e1049be36231b705916f 100644
--- a/flatland/envs/agent_chains.py
+++ b/flatland/envs/agent_chains.py
@@ -12,6 +12,9 @@ class MotionCheck(object):
"""
def __init__(self):
self.G = nx.DiGraph()
+ self.nDeadlocks = 0
+ self.svDeadlocked = set()
+
def addAgent(self, iAg, rc1, rc2, xlabel=None):
""" add an agent and its motion as row,col tuples of current and next position.
@@ -60,6 +63,10 @@ class MotionCheck(object):
return svStops
def find_stop_preds(self, svStops=None):
+ """ Find the predecessors to a list of stopped agents (ie the nodes / vertices)
+ Returns the set of predecessors.
+ Includes "chained" predecessors.
+ """
if svStops is None:
svStops = self.find_stops2()
@@ -73,9 +80,10 @@ class MotionCheck(object):
for oWCC in lWCC:
#print("Component:", oWCC)
+ # Get the node details for this WCC in a subgraph
Gwcc = self.G.subgraph(oWCC)
- # Find all the stops in this chain
+ # Find all the stops in this chain or tree
svCompStops = svStops.intersection(Gwcc)
#print(svCompStops)
@@ -91,11 +99,14 @@ class MotionCheck(object):
lStops = list(iter_stops)
svBlocked.update(lStops)
+ # the set of all the nodes/agents blocked by this set of stopped nodes
return svBlocked
def find_swaps(self):
""" find all the swap conflicts where two agents are trying to exchange places.
These appear as simple cycles of length 2.
+ These agents are necessarily deadlocked (since they can't change direction in flatland) -
+ meaning they will now be stuck for the rest of the episode.
"""
#svStops = self.find_stops2()
llvLoops = list(nx.algorithms.cycles.simple_cycles(self.G))
@@ -109,30 +120,73 @@ class MotionCheck(object):
"""
pass
+ def block_preds(self, svStops, color="red"):
+ """ Take a list of stopped agents, and apply a stop color to any chains/trees
+ of agents trying to head toward those cells.
+ Count the number of agents blocked, ignoring those which are already marked.
+ (Otherwise it can double count swaps)
+
+ """
+ iCount = 0
+ svBlocked = set()
+ # The reversed graph allows us to follow directed edges to find affected agents.
+ Grev = self.G.reverse()
+ for v in svStops:
+
+ # Use depth-first-search to find a tree of agents heading toward the blocked cell.
+ lvPred = list(nx.traversal.dfs_postorder_nodes(Grev, source=v))
+ svBlocked |= set(lvPred)
+ svBlocked.add(v)
+ #print("node:", v, "set", svBlocked)
+ # only count those not already marked
+ for v2 in [v]+lvPred:
+ if self.G.nodes[v2].get("color") != color:
+ self.G.nodes[v2]["color"] = color
+ iCount += 1
+
+ return svBlocked
+
+
def find_conflicts(self):
- svStops = self.find_stops2() # { u for u,v in nx.classes.function.selfloop_edges(self.G) }
- #llvLoops = list(nx.algorithms.cycles.simple_cycles(self.G))
- #llvSwaps = [lvLoop for lvLoop in llvLoops if len(lvLoop) == 2 ]
- #svSwaps = { v for lvSwap in llvSwaps for v in lvSwap }
- svSwaps = self.find_swaps()
- svBlocked = self.find_stop_preds(svStops.union(svSwaps))
+ svStops = self.find_stops2() # voluntarily stopped agents - have self-loops
+ svSwaps = self.find_swaps() # deadlocks - adjacent head-on collisions
+
+ # Block all swaps and their tree of predessors
+ self.svDeadlocked = self.block_preds(svSwaps, color="purple")
+ # Take the union of the above, and find all the predecessors
+ #svBlocked = self.find_stop_preds(svStops.union(svSwaps))
+
+ # Just look for the the tree of preds for each voluntarily stopped agent
+ svBlocked = self.find_stop_preds(svStops)
+
+ # iterate the nodes v with their predecessors dPred (dict of nodes->{})
for (v, dPred) in self.G.pred.items():
- if v in svSwaps:
- self.G.nodes[v]["color"] = "purple"
- elif v in svBlocked:
+ # mark any swaps with purple - these are directly deadlocked
+ #if v in svSwaps:
+ # self.G.nodes[v]["color"] = "purple"
+ # If they are not directly deadlocked, but are in the union of stopped + deadlocked
+ #elif v in svBlocked:
+
+ # if in blocked, it will not also be in a swap pred tree, so no need to worry about overwriting
+ if v in svBlocked:
self.G.nodes[v]["color"] = "red"
+ # not blocked but has two or more predecessors, ie >=2 agents waiting to enter this node
elif len(dPred)>1:
- if self.G.nodes[v].get("color") == "red":
+ # if this agent is already red/blocked, ignore. CHECK: why?
+ # certainly we want to ignore purple so we don't overwrite with red.
+ if self.G.nodes[v].get("color") in ("red", "purple"):
continue
+ # if this node has no agent, and >=2 want to enter it.
if self.G.nodes[v].get("agent") is None:
self.G.nodes[v]["color"] = "blue"
+ # this node has an agent and >=2 want to enter
else:
self.G.nodes[v]["color"] = "magenta"
- # predecessors of a contended cell
+ # predecessors of a contended cell: {agent index -> node}
diAgCell = {self.G.nodes[vPred].get("agent"): vPred for vPred in dPred}
# remove the agent with the lowest index, who wins
@@ -140,13 +194,15 @@ class MotionCheck(object):
diAgCell.pop(iAgWinner)
# Block all the remaining predessors, and their tree of preds
- for iAg, v in diAgCell.items():
- self.G.nodes[v]["color"] = "red"
- for vPred in nx.traversal.dfs_postorder_nodes(self.G.reverse(), source=v):
- self.G.nodes[vPred]["color"] = "red"
+ #for iAg, v in diAgCell.items():
+ # self.G.nodes[v]["color"] = "red"
+ # for vPred in nx.traversal.dfs_postorder_nodes(self.G.reverse(), source=v):
+ # self.G.nodes[vPred]["color"] = "red"
+ self.block_preds(diAgCell.values(), "red")
def check_motion(self, iAgent, rcPos):
- """ If agent position is None, we use a dummy position of (-1, iAgent)
+ """ Returns tuple of boolean can the agent move, and the cell it will move into.
+ If agent position is None, we use a dummy position of (-1, iAgent)
"""
if rcPos is None:
@@ -168,7 +224,7 @@ class MotionCheck(object):
# This should never happen - only the next cell of an agent has no successor
if len(dSucc)==0:
- print(f"error condition - agent {iAg} node {rcPos} has no successor")
+ print(f"error condition - agent {iAgent} node {rcPos} has no successor")
return (False, rcPos)
# This agent has a successor
@@ -181,6 +237,7 @@ class MotionCheck(object):
+
def render(omc:MotionCheck, horizontal=True):
try:
oAG = nx.drawing.nx_agraph.to_agraph(omc.G)
diff --git a/flatland/envs/agent_utils.py b/flatland/envs/agent_utils.py
index d0f9d941f2ba34ce33796b5dabfdb3d0b346c958..ef0701ac2abe2cc87e329a02120db6b1ca348584 100644
--- a/flatland/envs/agent_utils.py
+++ b/flatland/envs/agent_utils.py
@@ -119,8 +119,20 @@ class EnvAgent:
def load_legacy_static_agent(cls, static_agents_data: Tuple):
agents = []
for i, static_agent in enumerate(static_agents_data):
- agent = EnvAgent(initial_position=static_agent[0], initial_direction=static_agent[1],
- direction=static_agent[1], target=static_agent[2], moving=static_agent[3],
- speed_data=static_agent[4], malfunction_data=static_agent[5], handle=i)
+ if len(static_agent) >= 6:
+ agent = EnvAgent(initial_position=static_agent[0], initial_direction=static_agent[1],
+ direction=static_agent[1], target=static_agent[2], moving=static_agent[3],
+ speed_data=static_agent[4], malfunction_data=static_agent[5], handle=i)
+ else:
+ agent = EnvAgent(initial_position=static_agent[0], initial_direction=static_agent[1],
+ direction=static_agent[1], target=static_agent[2],
+ moving=False,
+ speed_data={"speed":1., "position_fraction":0., "transition_action_on_cell_exit":0.},
+ malfunction_data={
+ 'malfunction': 0,
+ 'nr_malfunctions': 0,
+ 'moving_before_malfunction': False
+ },
+ handle=i)
agents.append(agent)
return agents
diff --git a/flatland/envs/malfunction_generators.py b/flatland/envs/malfunction_generators.py
index 62a920805d2b530c5da2f26b9289e3a84e15cc02..0d27913d6f27fb5df301960655d90baa42ef1ac0 100644
--- a/flatland/envs/malfunction_generators.py
+++ b/flatland/envs/malfunction_generators.py
@@ -8,13 +8,17 @@ from numpy.random.mtrand import RandomState
from flatland.envs.agent_utils import EnvAgent, RailAgentStatus
from flatland.envs import persistence
-Malfunction = NamedTuple('Malfunction', [('num_broken_steps', int)])
+
+# why do we have both MalfunctionParameters and MalfunctionProcessData - they are both the same!
MalfunctionParameters = NamedTuple('MalfunctionParameters',
[('malfunction_rate', float), ('min_duration', int), ('max_duration', int)])
-MalfunctionGenerator = Callable[[EnvAgent, RandomState, bool], Optional[Malfunction]]
MalfunctionProcessData = NamedTuple('MalfunctionProcessData',
[('malfunction_rate', float), ('min_duration', int), ('max_duration', int)])
+Malfunction = NamedTuple('Malfunction', [('num_broken_steps', int)])
+
+# Why is the return value Optional? We always return a Malfunction.
+MalfunctionGenerator = Callable[[EnvAgent, RandomState, bool], Optional[Malfunction]]
def _malfunction_prob(rate: float) -> float:
"""
@@ -27,6 +31,146 @@ def _malfunction_prob(rate: float) -> float:
else:
return 1 - np.exp(-rate)
+class ParamMalfunctionGen(object):
+ """ Preserving old behaviour of using MalfunctionParameters for constructor,
+ but returning MalfunctionProcessData in get_process_data.
+ Data structure and content is the same.
+ """
+ def __init__(self, parameters: MalfunctionParameters):
+ #self.mean_malfunction_rate = parameters.malfunction_rate
+ #self.min_number_of_steps_broken = parameters.min_duration
+ #self.max_number_of_steps_broken = parameters.max_duration
+ self.MFP = parameters
+
+ def generate(self,
+ agent: EnvAgent = None,
+ np_random: RandomState = None,
+ reset=False) -> Optional[Malfunction]:
+
+ # Dummy reset function as we don't implement specific seeding here
+ if reset:
+ return Malfunction(0)
+
+ if agent.malfunction_data['malfunction'] < 1:
+ if np_random.rand() < _malfunction_prob(self.MFP.malfunction_rate):
+ num_broken_steps = np_random.randint(self.MFP.min_duration,
+ self.MFP.max_duration + 1) + 1
+ return Malfunction(num_broken_steps)
+ return Malfunction(0)
+
+ def get_process_data(self):
+ return MalfunctionProcessData(*self.MFP)
+
+
+class NoMalfunctionGen(ParamMalfunctionGen):
+ def __init__(self):
+ super().__init__(MalfunctionParameters(0,0,0))
+
+class FileMalfunctionGen(ParamMalfunctionGen):
+ def __init__(self, env_dict=None, filename=None, load_from_package=None):
+ """ uses env_dict if populated, otherwise tries to load from file / package.
+ """
+ if env_dict is None:
+ env_dict = persistence.RailEnvPersister.load_env_dict(filename, load_from_package=load_from_package)
+
+ if "malfunction" in env_dict:
+ oMFP = MalfunctionParameters(*env_dict["malfunction"])
+ else:
+ oMFP = MalfunctionParameters(0,0,0) # no malfunctions
+ super().__init__(oMFP)
+
+
+################################################################################################
+# OLD / DEPRECATED generator functions below. To be removed.
+
+def no_malfunction_generator() -> Tuple[MalfunctionGenerator, MalfunctionProcessData]:
+ """
+ Malfunction generator which generates no malfunctions
+
+ Parameters
+ ----------
+ Nothing
+
+ Returns
+ -------
+ generator, Tuple[float, int, int] with mean_malfunction_rate, min_number_of_steps_broken, max_number_of_steps_broken
+ """
+ print("DEPRECATED - use NoMalfunctionGen instead of no_malfunction_generator")
+ # Mean malfunction in number of time steps
+ mean_malfunction_rate = 0.
+
+ # Uniform distribution parameters for malfunction duration
+ min_number_of_steps_broken = 0
+ max_number_of_steps_broken = 0
+
+ def generator(agent: EnvAgent = None, np_random: RandomState = None, reset=False) -> Optional[Malfunction]:
+ return Malfunction(0)
+
+ return generator, MalfunctionProcessData(mean_malfunction_rate, min_number_of_steps_broken,
+ max_number_of_steps_broken)
+
+
+
+def single_malfunction_generator(earlierst_malfunction: int, malfunction_duration: int) -> Tuple[
+ MalfunctionGenerator, MalfunctionProcessData]:
+ """
+ Malfunction generator which guarantees exactly one malfunction during an episode of an ACTIVE agent.
+
+ Parameters
+ ----------
+ earlierst_malfunction: Earliest possible malfunction onset
+ malfunction_duration: The duration of the single malfunction
+
+ Returns
+ -------
+ generator, Tuple[float, int, int] with mean_malfunction_rate, min_number_of_steps_broken, max_number_of_steps_broken
+ """
+ # Mean malfunction in number of time steps
+ mean_malfunction_rate = 0.
+
+ # Uniform distribution parameters for malfunction duration
+ min_number_of_steps_broken = 0
+ max_number_of_steps_broken = 0
+
+ # Keep track of the total number of malfunctions in the env
+ global_nr_malfunctions = 0
+
+ # Malfunction calls per agent
+ malfunction_calls = dict()
+
+ def generator(agent: EnvAgent = None, np_random: RandomState = None, reset=False) -> Optional[Malfunction]:
+ # We use the global variable to assure only a single malfunction in the env
+ nonlocal global_nr_malfunctions
+ nonlocal malfunction_calls
+
+ # Reset malfunciton generator
+ if reset:
+ nonlocal global_nr_malfunctions
+ nonlocal malfunction_calls
+ global_nr_malfunctions = 0
+ malfunction_calls = dict()
+ return Malfunction(0)
+
+ # No more malfunctions if we already had one, ignore all updates
+ if global_nr_malfunctions > 0:
+ return Malfunction(0)
+
+ # Update number of calls per agent
+ if agent.handle in malfunction_calls:
+ malfunction_calls[agent.handle] += 1
+ else:
+ malfunction_calls[agent.handle] = 1
+
+ # Break an agent that is active at the time of the malfunction
+ if agent.status == RailAgentStatus.ACTIVE and malfunction_calls[agent.handle] >= earlierst_malfunction:
+ global_nr_malfunctions += 1
+ return Malfunction(malfunction_duration)
+ else:
+ return Malfunction(0)
+
+ return generator, MalfunctionProcessData(mean_malfunction_rate, min_number_of_steps_broken,
+ max_number_of_steps_broken)
+
def malfunction_from_file(filename: str, load_from_package=None) -> Tuple[MalfunctionGenerator, MalfunctionProcessData]:
"""
@@ -40,13 +184,9 @@ def malfunction_from_file(filename: str, load_from_package=None) -> Tuple[Malfun
-------
generator, Tuple[float, int, int] with mean_malfunction_rate, min_number_of_steps_broken, max_number_of_steps_broken
"""
- # with open(filename, "rb") as file_in:
- # load_data = file_in.read()
- # if filename.endswith("mpk"):
- # data = msgpack.unpackb(load_data, use_list=False, encoding='utf-8')
- # elif filename.endswith("pkl"):
- # data = pickle.loads(load_data)
+ print("DEPRECATED - use FileMalfunctionGen instead of malfunction_from_file")
+
env_dict = persistence.RailEnvPersister.load_env_dict(filename, load_from_package=load_from_package)
# TODO: make this better by using namedtuple in the pickle file. See issue 282
if "malfunction" in env_dict:
@@ -111,6 +251,9 @@ def malfunction_from_params(parameters: MalfunctionParameters) -> Tuple[Malfunct
-------
generator, Tuple[float, int, int] with mean_malfunction_rate, min_number_of_steps_broken, max_number_of_steps_broken
"""
+
+ print("DEPRECATED - use ParamMalfunctionGen instead of malfunction_from_params")
+
mean_malfunction_rate = parameters.malfunction_rate
min_number_of_steps_broken = parameters.min_duration
max_number_of_steps_broken = parameters.max_duration
@@ -142,128 +285,3 @@ def malfunction_from_params(parameters: MalfunctionParameters) -> Tuple[Malfunct
return generator, MalfunctionProcessData(mean_malfunction_rate, min_number_of_steps_broken,
max_number_of_steps_broken)
-
-class ParamMalfunctionGen(object):
- def __init__(self, parameters: MalfunctionParameters):
- self.mean_malfunction_rate = parameters.malfunction_rate
- self.min_number_of_steps_broken = parameters.min_duration
- self.max_number_of_steps_broken = parameters.max_duration
-
- def generate(self, agent: EnvAgent = None, np_random: RandomState = None, reset=False) -> Optional[Malfunction]:
-
- # Dummy reset function as we don't implement specific seeding here
- if reset:
- return Malfunction(0)
-
- if agent.malfunction_data['malfunction'] < 1:
- if np_random.rand() < _malfunction_prob(self.mean_malfunction_rate):
- num_broken_steps = np_random.randint(self.min_number_of_steps_broken,
- self.max_number_of_steps_broken + 1) + 1
- return Malfunction(num_broken_steps)
- return Malfunction(0)
-
- def get_process_data(self):
- return MalfunctionProcessData(
- self.mean_malfunction_rate,
- self.min_number_of_steps_broken,
- self.max_number_of_steps_broken)
-
-
-class NoMalfunctionGen(ParamMalfunctionGen):
- def __init__(self):
- self.mean_malfunction_rate = 0.
- self.min_number_of_steps_broken = 0
- self.max_number_of_steps_broken = 0
-
- def generate(self, agent: EnvAgent = None, np_random: RandomState = None, reset=False) -> Optional[Malfunction]:
- return Malfunction(0)
-
-
-
-
-def no_malfunction_generator() -> Tuple[MalfunctionGenerator, MalfunctionProcessData]:
- """
- Malfunction generator which generates no malfunctions
-
- Parameters
- ----------
- Nothing
-
- Returns
- -------
- generator, Tuple[float, int, int] with mean_malfunction_rate, min_number_of_steps_broken, max_number_of_steps_broken
- """
- # Mean malfunction in number of time steps
- mean_malfunction_rate = 0.
-
- # Uniform distribution parameters for malfunction duration
- min_number_of_steps_broken = 0
- max_number_of_steps_broken = 0
-
- def generator(agent: EnvAgent = None, np_random: RandomState = None, reset=False) -> Optional[Malfunction]:
- return Malfunction(0)
-
- return generator, MalfunctionProcessData(mean_malfunction_rate, min_number_of_steps_broken,
- max_number_of_steps_broken)
-
-
-
-def single_malfunction_generator(earlierst_malfunction: int, malfunction_duration: int) -> Tuple[
- MalfunctionGenerator, MalfunctionProcessData]:
- """
- Malfunction generator which guarantees exactly one malfunction during an episode of an ACTIVE agent.
-
- Parameters
- ----------
- earlierst_malfunction: Earliest possible malfunction onset
- malfunction_duration: The duration of the single malfunction
-
- Returns
- -------
- generator, Tuple[float, int, int] with mean_malfunction_rate, min_number_of_steps_broken, max_number_of_steps_broken
- """
- # Mean malfunction in number of time steps
- mean_malfunction_rate = 0.
-
- # Uniform distribution parameters for malfunction duration
- min_number_of_steps_broken = 0
- max_number_of_steps_broken = 0
-
- # Keep track of the total number of malfunctions in the env
- global_nr_malfunctions = 0
-
- # Malfunction calls per agent
- malfunction_calls = dict()
-
- def generator(agent: EnvAgent = None, np_random: RandomState = None, reset=False) -> Optional[Malfunction]:
- # We use the global variable to assure only a single malfunction in the env
- nonlocal global_nr_malfunctions
- nonlocal malfunction_calls
-
- # Reset malfunciton generator
- if reset:
- nonlocal global_nr_malfunctions
- nonlocal malfunction_calls
- global_nr_malfunctions = 0
- malfunction_calls = dict()
- return Malfunction(0)
-
- # No more malfunctions if we already had one, ignore all updates
- if global_nr_malfunctions > 0:
- return Malfunction(0)
-
- # Update number of calls per agent
- if agent.handle in malfunction_calls:
- malfunction_calls[agent.handle] += 1
- else:
- malfunction_calls[agent.handle] = 1
-
- # Break an agent that is active at the time of the malfunction
- if agent.status == RailAgentStatus.ACTIVE and malfunction_calls[agent.handle] >= earlierst_malfunction:
- global_nr_malfunctions += 1
- return Malfunction(malfunction_duration)
- else:
- return Malfunction(0)
-
- return generator, MalfunctionProcessData(mean_malfunction_rate, min_number_of_steps_broken,
- max_number_of_steps_broken)
diff --git a/flatland/envs/persistence.py b/flatland/envs/persistence.py
index 8ed7ec8ab27dbc08746bd2a774dd130fc8eefdce..bc4b169b1aad3893d96d82cb8284b369f13104f2 100644
--- a/flatland/envs/persistence.py
+++ b/flatland/envs/persistence.py
@@ -2,6 +2,7 @@
import pickle
import msgpack
+import msgpack_numpy
import numpy as np
from flatland.envs import rail_env
@@ -22,6 +23,7 @@ from flatland.envs import malfunction_generators as mal_gen
from flatland.envs import rail_generators as rail_gen
from flatland.envs import schedule_generators as sched_gen
+msgpack_numpy.patch()
class RailEnvPersister(object):
@@ -111,15 +113,20 @@ class RailEnvPersister(object):
env_dict = cls.load_env_dict(filename, load_from_package=load_from_package)
+ llGrid = env_dict["grid"]
+ height = len(llGrid)
+ width = len(llGrid[0])
# TODO: inefficient - each one of these generators loads the complete env file.
- env = rail_env.RailEnv(width=1, height=1,
+ env = rail_env.RailEnv(#width=1, height=1,
+ width=width, height=height,
rail_generator=rail_gen.rail_from_file(filename,
load_from_package=load_from_package),
schedule_generator=sched_gen.schedule_from_file(filename,
load_from_package=load_from_package),
- malfunction_generator_and_process_data=mal_gen.malfunction_from_file(filename,
- load_from_package=load_from_package),
+ #malfunction_generator_and_process_data=mal_gen.malfunction_from_file(filename,
+ # load_from_package=load_from_package),
+ malfunction_generator=mal_gen.FileMalfunctionGen(env_dict),
obs_builder_object=DummyObservationBuilder(),
record_steps=True)
@@ -141,7 +148,11 @@ class RailEnvPersister(object):
if filename.endswith("mpk"):
env_dict = msgpack.unpackb(load_data, use_list=False, encoding="utf-8")
elif filename.endswith("pkl"):
- env_dict = pickle.loads(load_data)
+ try:
+ env_dict = pickle.loads(load_data)
+ except ValueError:
+ print("pickle failed to load file:", filename, " trying msgpack (deprecated)...")
+ env_dict = msgpack.unpackb(load_data, use_list=False, encoding="utf-8")
else:
print(f"filename {filename} must end with either pkl or mpk")
env_dict = {}
diff --git a/flatland/envs/rail_env.py b/flatland/envs/rail_env.py
index b6d78f50d64e3f72f1606afefe62177c8eb8ca5d..94d911eaab8dfe545d6960f9cc7068e53fee8e16 100644
--- a/flatland/envs/rail_env.py
+++ b/flatland/envs/rail_env.py
@@ -728,6 +728,8 @@ class RailEnv(Environment):
self.rewards_dict[i_agent] += self.step_penalty * agent.speed_data['speed']
def _step_agent_cf(self, i_agent, action: Optional[RailEnvActions] = None):
+ """ "close following" version of step_agent.
+ """
agent = self.agents[i_agent]
if agent.status in [RailAgentStatus.DONE, RailAgentStatus.DONE_REMOVED]: # this agent has already completed...
return
@@ -748,6 +750,8 @@ class RailEnv(Environment):
# if agent is broken, actions are ignored and agent does not move.
# full step penalty in this case
+ # TODO: this means that deadlocked agents which suffer a malfunction are marked as
+ # stopped rather than deadlocked.
if agent.malfunction_data['malfunction'] > 0:
self.motionCheck.addAgent(i_agent, agent.position, agent.position)
# agent will get penalty in step_agent2_cf
@@ -996,8 +1000,12 @@ class RailEnv(Environment):
else:
pos = (int(agent.position[0]), int(agent.position[1]))
# print("pos:", pos, type(pos[0]))
- list_agents_state.append(
- [*pos, int(agent.direction), agent.malfunction_data["malfunction"]])
+ list_agents_state.append([
+ *pos, int(agent.direction),
+ agent.malfunction_data["malfunction"],
+ int(agent.status),
+ int(agent.position in self.motionCheck.svDeadlocked)
+ ])
self.cur_episode.append(list_agents_state)
self.list_actions.append(dActions)
diff --git a/flatland/envs/schedule_generators.py b/flatland/envs/schedule_generators.py
index 7d5656ddcbdd63ddc17c07aeae52419da0239a7d..c1789435fbca195f2a02035e481fbbf95a826407 100644
--- a/flatland/envs/schedule_generators.py
+++ b/flatland/envs/schedule_generators.py
@@ -323,6 +323,10 @@ def schedule_from_file(filename, load_from_package=None) -> ScheduleGenerator:
env_dict = persistence.RailEnvPersister.load_env_dict(filename, load_from_package=load_from_package)
max_episode_steps = env_dict.get("max_episode_steps", 0)
+ if (max_episode_steps==0):
+ print("This env file has no max_episode_steps (deprecated) - setting to 100")
+ max_episode_steps = 100
+
agents = env_dict["agents"]
#print("schedule generator from_file - agents: ", agents)
@@ -335,6 +339,8 @@ def schedule_from_file(filename, load_from_package=None) -> ScheduleGenerator:
agents_direction = [a.initial_direction for a in agents]
agents_target = [a.target for a in agents]
agents_speed = [a.speed_data['speed'] for a in agents]
+
+ # Malfunctions from here are not used. They have their own generator.
#agents_malfunction = [a.malfunction_data['malfunction_rate'] for a in agents]
return Schedule(agent_positions=agents_position, agent_directions=agents_direction,
diff --git a/flatland/evaluators/service.py b/flatland/evaluators/service.py
index 7eef7f0667335cddd233a17327ea06960e8cfc97..03b94380f232773044a2733802e6df4ef9d1918f 100644
--- a/flatland/evaluators/service.py
+++ b/flatland/evaluators/service.py
@@ -48,18 +48,39 @@ m.patch()
########################################################
# CONSTANTS
########################################################
+
+# Don't proceed to next Test if the previous one didn't reach this mean completion percentage
+TEST_MIN_PERCENTAGE_COMPLETE_MEAN = float(os.getenv("TEST_MIN_PERCENTAGE_COMPLETE_MEAN", 0.25))
+
+# After this number of consecutive timeouts, kill the submission:
+# this probably means the submission has crashed
+MAX_SUCCESSIVE_TIMEOUTS = int(os.getenv("FLATLAND_MAX_SUCCESSIVE_TIMEOUTS", 10))
+
+debug_mode = (os.getenv("AICROWD_DEBUG_SUBMISSION", 0) == 1)
+if debug_mode:
+ print("=" * 20)
+ print("Submission in DEBUG MODE! will get limited time")
+ print("=" * 20)
+
+# 8 hours (will get debug timeout from env variable if applicable)
+OVERALL_TIMEOUT = int(os.getenv(
+ "FLATLAND_OVERALL_TIMEOUT",
+ 8 * 60 * 60))
+
+# 10 mins
INTIAL_PLANNING_TIMEOUT = int(os.getenv(
"FLATLAND_INITIAL_PLANNING_TIMEOUT",
- 5 * 60)) # 5 mins
+ 10 * 60))
+
+# 10 seconds
PER_STEP_TIMEOUT = int(os.getenv(
"FLATLAND_PER_STEP_TIMEOUT",
- 5)) # 5 seconds
+ 10))
+
+# 5 min - applies to the rest of the commands
DEFAULT_COMMAND_TIMEOUT = int(os.getenv(
"FLATLAND_DEFAULT_COMMAND_TIMEOUT",
- 1 * 60)) # 1 min
-# This applies to the rest of the commands
-
-MAX_SUCCESSIVE_TIMEOUTS = 10
+ 5 * 60))
RANDOM_SEED = int(os.getenv("FLATLAND_EVALUATION_RANDOM_SEED", 1001))
@@ -90,56 +111,66 @@ class FlatlandRemoteEvaluationService:
numpy arrays).
"""
- def __init__(self,
- test_env_folder="/tmp",
- flatland_rl_service_id='FLATLAND_RL_SERVICE_ID',
- remote_host='127.0.0.1',
- remote_port=6379,
- remote_db=0,
- remote_password=None,
- visualize=False,
- video_generation_envs=[],
- report=None,
- verbose=False,
- actionDir=None,
- episodeDir=None,
- mergeDir=None,
- use_pickle=False,
- shuffle=True,
- missing_only=False,
- result_output_path=None,
- disable_timeouts=False
- ):
+ def __init__(
+ self,
+ test_env_folder="/tmp",
+ flatland_rl_service_id='FLATLAND_RL_SERVICE_ID',
+ remote_host='127.0.0.1',
+ remote_port=6379,
+ remote_db=0,
+ remote_password=None,
+ visualize=False,
+ video_generation_envs=[],
+ report=None,
+ verbose=False,
+ action_dir=None,
+ episode_dir=None,
+ merge_dir=None,
+ use_pickle=False,
+ shuffle=False,
+ missing_only=False,
+ result_output_path=None,
+ disable_timeouts=False
+ ):
# Episode recording properties
- self.actionDir = actionDir
- if actionDir and not os.path.exists(self.actionDir):
- os.makedirs(self.actionDir)
- self.episodeDir = episodeDir
- if episodeDir and not os.path.exists(self.episodeDir):
- os.makedirs(self.episodeDir)
- self.mergeDir = mergeDir
- if mergeDir and not os.path.exists(self.mergeDir):
- os.makedirs(self.mergeDir)
+ self.action_dir = action_dir
+ if action_dir and not os.path.exists(self.action_dir):
+ os.makedirs(self.action_dir)
+ self.episode_dir = episode_dir
+ if episode_dir and not os.path.exists(self.episode_dir):
+ os.makedirs(self.episode_dir)
+ self.merge_dir = merge_dir
+ if merge_dir and not os.path.exists(self.merge_dir):
+ os.makedirs(self.merge_dir)
self.use_pickle = use_pickle
self.missing_only = missing_only
- self.disable_timeouts = disable_timeouts
+ self.episode_actions = []
+ self.disable_timeouts = disable_timeouts
if self.disable_timeouts:
print("=" * 20)
print("Timeout are DISABLED!")
print("=" * 20)
- if not shuffle:
+ if shuffle:
print("=" * 20)
- print("Env shuffling is DISABLED!")
+ print("Env shuffling is ENABLED! not suitable for infinite wave")
print("=" * 20)
+ print("=" * 20)
+ print("Max pre-planning time:", INTIAL_PLANNING_TIMEOUT)
+ print("Max step time:", PER_STEP_TIMEOUT)
+ print("Max overall time:", OVERALL_TIMEOUT)
+ print("Max submission startup time:", DEFAULT_COMMAND_TIMEOUT)
+ print("Max consecutive timeouts:", MAX_SUCCESSIVE_TIMEOUTS)
+ print("=" * 20)
+
# Test Env folder Paths
self.test_env_folder = test_env_folder
self.video_generation_envs = video_generation_envs
self.env_file_paths = self.get_env_filepaths()
-
+ print(self.env_file_paths)
# Shuffle all the env_file_paths for more exciting videos
# and for more uniform time progression
if shuffle:
@@ -153,13 +184,13 @@ class FlatlandRemoteEvaluationService:
self.report = report
# Use a state to swallow and ignore any steps after an env times out.
- # this should be reset to False after env reset() to get the next env.
self.state_env_timed_out = False
# Count the number of successive timeouts (will kill after MAX_SUCCESSIVE_TIMEOUTS)
# This prevents a crashed submission to keep running forever
self.timeout_counter = 0
+ # Results are the metrics: percent done, rewards, timing...
self.result_output_path = result_output_path
# Communication Protocol Related vars
@@ -169,7 +200,6 @@ class FlatlandRemoteEvaluationService:
self.namespace,
self.service_id
)
-
self.error_channel = "{}::{}::errors".format(
self.namespace,
self.service_id
@@ -212,12 +242,19 @@ class FlatlandRemoteEvaluationService:
self.simulation_rewards = []
self.simulation_rewards_normalized = []
self.simulation_percentage_complete = []
+ self.simulation_percentage_complete_per_test = {}
self.simulation_steps = []
self.simulation_times = []
self.env_step_times = []
self.nb_malfunctioning_trains = []
+ self.nb_deadlocked_trains = []
+ self.overall_start_time = 0
+ self.termination_cause = "No reported termination cause."
+ self.evaluation_done = False
self.begin_simulation = False
self.current_step = 0
+ self.current_test = -1
+ self.current_level = -1
self.visualize = visualize
self.vizualization_folder_name = "./.visualizations"
self.record_frame_step = 0
@@ -232,7 +269,7 @@ class FlatlandRemoteEvaluationService:
def update_running_stats(self, key, scalar):
"""
- Computes the running mean for certain params
+ Computes the running min/mean/max for given param
"""
mean_key = "{}_mean".format(key)
counter_key = "{}_counter".format(key)
@@ -293,40 +330,47 @@ class FlatlandRemoteEvaluationService:
  ├── .......
└── Level_99.pkl
"""
- env_paths = sorted(
- glob.glob(
- os.path.join(
- self.test_env_folder,
- "*/*.pkl"
- )
+ env_paths = glob.glob(
+ os.path.join(
+ self.test_env_folder,
+ "*/*.pkl"
)
)
# Remove the root folder name from the individual
# lists, so that we only have the path relative
# to the test root folder
- env_paths = sorted([os.path.relpath(
- x, self.test_env_folder
- ) for x in env_paths])
-
- # Sort in proper order
- def get_file_order(f):
- numbers = re.findall(r'\d+', os.path.relpath(f))
- value = int(numbers[0]) * 1000 + int(numbers[1])
+ env_paths = [os.path.relpath(x, self.test_env_folder) for x in env_paths]
+
+ # Sort in proper numerical order
+ def get_file_order(filename):
+ test_id, level_id = self.get_env_test_and_level(filename)
+ value = test_id * 1000 + level_id
return value
env_paths.sort(key=get_file_order)
# if requested, only generate actions for those envs which don't already have them
- if self.mergeDir and self.missing_only:
+ if self.merge_dir and self.missing_only:
existing_paths = (itertools.chain.from_iterable(
- [glob.glob(os.path.join(self.mergeDir, f"envs/*.{ext}"))
+ [glob.glob(os.path.join(self.merge_dir, f"envs/*.{ext}"))
for ext in ["pkl", "mpk"]]))
- existing_paths = [os.path.relpath(sPath, self.mergeDir) for sPath in existing_paths]
+ existing_paths = [os.path.relpath(sPath, self.merge_dir) for sPath in existing_paths]
env_paths = set(env_paths) - set(existing_paths)
return env_paths
+ def get_env_test_and_level(self, filename):
+ numbers = re.findall(r'\d+', os.path.relpath(filename))
+
+ if len(numbers) == 2:
+ test_id = int(numbers[0])
+ level_id = int(numbers[1])
+ else:
+ print(numbers)
+ raise ValueError("Unexpected file path, expects 'Test_<N>/Level_<M>.pkl', found", filename)
+ return test_id, level_id
+
def instantiate_evaluation_metadata(self):
"""
This instantiates a pandas dataframe to record
@@ -355,6 +399,7 @@ class FlatlandRemoteEvaluationService:
self.evaluation_metadata_df["steps"] = np.nan
self.evaluation_metadata_df["simulation_time"] = np.nan
self.evaluation_metadata_df["nb_malfunctioning_trains"] = np.nan
+ self.evaluation_metadata_df["nb_deadlocked_trains"] = np.nan
# Add client specific columns
# TODO: This needs refactoring
@@ -378,34 +423,61 @@ class FlatlandRemoteEvaluationService:
last_simulation_env_file_path
]
- _row.reward = self.simulation_rewards[-1]
- _row.normalized_reward = self.simulation_rewards_normalized[-1]
- _row.percentage_complete = self.simulation_percentage_complete[-1]
- _row.steps = self.simulation_steps[-1]
- _row.simulation_time = self.simulation_times[-1]
- _row.nb_malfunctioning_trains = self.nb_malfunctioning_trains[-1]
-
- # TODO: This needs refactoring
# Add controller_inference_time_metrics
# These metrics may be missing if no step was done before the episode finished
- if "current_episode_controller_inference_time_min" in self.stats:
- _row.controller_inference_time_min = self.stats[
- "current_episode_controller_inference_time_min"
- ]
- _row.controller_inference_time_mean = self.stats[
- "current_episode_controller_inference_time_mean"
- ]
- _row.controller_inference_time_max = self.stats[
- "current_episode_controller_inference_time_max"
- ]
- else:
- _row.controller_inference_time_min = 0.0
- _row.controller_inference_time_mean = 0.0
- _row.controller_inference_time_max = 0.0
- self.evaluation_metadata_df.loc[
- last_simulation_env_file_path
- ] = _row
+ # generate the lists of names for the stats (input names and output names)
+ sPrefixIn = "current_episode_controller_inference_time_"
+ sPrefixOut = "controller_inference_time_"
+ lsStatIn = [sPrefixIn + sStat for sStat in ["min", "mean", "max"]]
+ lsStatOut = [sPrefixOut + sStat for sStat in ["min", "mean", "max"]]
+
+ if lsStatIn[0] in self.stats:
+ lrStats = [self.stats[sStat] for sStat in lsStatIn]
+ else:
+ lrStats = [0.0] * len(lsStatIn)
+
+ lsFields = ("reward, normalized_reward, percentage_complete, " + \
+ "steps, simulation_time, nb_malfunctioning_trains, nb_deadlocked_trains").split(", ") + \
+ lsStatOut
+
+ loValues = [self.simulation_rewards[-1],
+ self.simulation_rewards_normalized[-1],
+ self.simulation_percentage_complete[-1],
+ self.simulation_steps[-1],
+ self.simulation_times[-1],
+ self.nb_malfunctioning_trains[-1],
+ self.nb_deadlocked_trains[-1]
+ ] + lrStats
+
+ # update the dataframe without the updating-a-copy warning
+ df = self.evaluation_metadata_df
+ df.loc[last_simulation_env_file_path, lsFields] = loValues
+
+ # _row.reward = self.simulation_rewards[-1]
+ # _row.normalized_reward = self.simulation_rewards_normalized[-1]
+ # _row.percentage_complete = self.simulation_percentage_complete[-1]
+ # _row.steps = self.simulation_steps[-1]
+ # _row.simulation_time = self.simulation_times[-1]
+ # _row.nb_malfunctioning_trains = self.nb_malfunctioning_trains[-1]
+
+ # _row.controller_inference_time_min = self.stats[
+ # "current_episode_controller_inference_time_min"
+ # ]
+ # _row.controller_inference_time_mean = self.stats[
+ # "current_episode_controller_inference_time_mean"
+ # ]
+ # _row.controller_inference_time_max = self.stats[
+ # "current_episode_controller_inference_time_max"
+ # ]
+ # else:
+ # _row.controller_inference_time_min = 0.0
+ # _row.controller_inference_time_mean = 0.0
+ # _row.controller_inference_time_max = 0.0
+
+ # self.evaluation_metadata_df.loc[
+ # last_simulation_env_file_path
+ # ] = _row
# Delete this key from the stats to ensure that it
# gets computed again from scratch in the next episode
@@ -587,10 +659,10 @@ class FlatlandRemoteEvaluationService:
def handle_env_create(self, command):
"""
Handles a ENV_CREATE command from the client
- TODO: Add a high level summary of everything thats happening here.
"""
- if not self.simulation_done:
- # trying to reset a simulation before finishing the previous one
+
+ # Check if the previous episode was finished
+ if not self.simulation_done and not self.evaluation_done:
_command_response = self._error_template("CAN'T CREATE NEW ENV BEFORE PREVIOUS IS DONE")
self.send_response(_command_response, command)
raise Exception(_command_response['payload'])
@@ -598,26 +670,61 @@ class FlatlandRemoteEvaluationService:
self.simulation_count += 1
self.simulation_done = False
+ if self.simulation_count == 0:
+ # Very first episode: start the overall timer
+ self.overall_start_time = time.time()
+
# reset the timeout flag / state.
self.state_env_timed_out = False
- if self.simulation_count < len(self.env_file_paths):
+ # Check if we have finished all the available envs
+ if self.simulation_count >= len(self.env_file_paths):
+ self.evaluation_done = True
+ # Hack - just ensure these are set
+ test_env_file_path = self.env_file_paths[self.simulation_count - 1]
+ env_test, env_level = self.get_env_test_and_level(test_env_file_path)
+ else:
+ test_env_file_path = self.env_file_paths[self.simulation_count]
+ env_test, env_level = self.get_env_test_and_level(test_env_file_path)
+
+ # Did we just finish a test, and if yes did it reach high enough mean percentage done?
+ if self.current_test != env_test and env_test != 0:
+ if self.current_test not in self.simulation_percentage_complete_per_test:
+ print("No environment was finished at all during test {}!".format(self.current_test))
+ mean_test_complete_percentage = 0.0
+ else:
+ mean_test_complete_percentage = np.mean(self.simulation_percentage_complete_per_test[self.current_test])
+
+ if mean_test_complete_percentage < TEST_MIN_PERCENTAGE_COMPLETE_MEAN:
+ print("=" * 15)
+ msg = "The mean percentage of done agents during the last Test ({} environments) was too low: {:.3f} < {}".format(
+ len(self.simulation_percentage_complete_per_test[self.current_test]),
+ mean_test_complete_percentage,
+ TEST_MIN_PERCENTAGE_COMPLETE_MEAN
+ )
+ print(msg, "Evaluation will stop.")
+ self.termination_cause = msg
+ self.evaluation_done = True
+
+ if self.simulation_count < len(self.env_file_paths) and not self.evaluation_done:
"""
There are still test envs left that are yet to be evaluated
"""
- test_env_file_path = self.env_file_paths[self.simulation_count]
+
+ print("=" * 15)
print("Evaluating {} ({}/{})".format(test_env_file_path, self.simulation_count, len(self.env_file_paths)))
+
test_env_file_path = os.path.join(
self.test_env_folder,
test_env_file_path
)
+
+ self.current_test = env_test
+ self.current_level = env_level
+
del self.env
- self.env = RailEnv(width=1, height=1,
- rail_generator=rail_from_file(test_env_file_path),
- schedule_generator=schedule_from_file(test_env_file_path),
- malfunction_generator_and_process_data=malfunction_from_file(test_env_file_path),
- obs_builder_object=DummyObservationBuilder(),
- record_steps=True)
+
+ self.env, _env_dict = RailEnvPersister.load_new(test_env_file_path)
self.begin_simulation = time.time()
@@ -634,6 +741,7 @@ class FlatlandRemoteEvaluationService:
self.simulation_rewards.append(0)
self.simulation_rewards_normalized.append(0)
self.simulation_percentage_complete.append(0)
+ self.simulation_times.append(0)
self.simulation_steps.append(0)
self.nb_malfunctioning_trains.append(0)
@@ -676,20 +784,23 @@ class FlatlandRemoteEvaluationService:
#####################################################################
# Update evaluation state
#####################################################################
+ elapsed = time.time() - self.overall_start_time
progress = np.clip(
- self.simulation_count * 1.0 / len(self.env_file_paths),
+ elapsed / OVERALL_TIMEOUT,
0, 1)
- mean_reward, mean_normalized_reward, mean_percentage_complete = self.compute_mean_scores()
+ mean_reward, mean_normalized_reward, sum_normalized_reward, mean_percentage_complete = self.compute_mean_scores()
self.evaluation_state["state"] = "IN_PROGRESS"
self.evaluation_state["progress"] = progress
self.evaluation_state["simulation_count"] = self.simulation_count
- self.evaluation_state["score"]["score"] = mean_percentage_complete
- self.evaluation_state["score"]["score_secondary"] = mean_reward
+ self.evaluation_state["score"]["score"] = sum_normalized_reward
+ self.evaluation_state["score"]["score_secondary"] = mean_percentage_complete
self.evaluation_state["meta"]["normalized_reward"] = mean_normalized_reward
+ self.evaluation_state["meta"]["termination_cause"] = self.termination_cause
self.handle_aicrowd_info_event(self.evaluation_state)
- self.lActions = []
+
+ self.episode_actions = []
def handle_env_step(self, command):
"""
@@ -697,20 +808,34 @@ class FlatlandRemoteEvaluationService:
TODO: Add a high level summary of everything thats happening here.
"""
- if self.state_env_timed_out:
- print("Ignoring step command after timeout")
+ if self.state_env_timed_out or self.evaluation_done:
+ print("Ignoring step command after timeout.")
return
_payload = command['payload']
if not self.env:
- raise Exception(
- "env_client.step called before env_client.env_create() call")
+ raise Exception("env_client.step called before env_client.env_create() call")
if self.env.dones['__all__']:
raise Exception(
"Client attempted to perform an action on an Env which \
has done['__all__']==True")
+ overall_elapsed = (time.time() - self.overall_start_time)
+ if overall_elapsed > OVERALL_TIMEOUT:
+ msg = "Reached overall time limit: took {:.2f}s, limit is {:.2f}s.".format(
+ overall_elapsed, OVERALL_TIMEOUT
+ )
+ self.termination_cause = msg
+ self.evaluation_done = True
+
+ print("=" * 15)
+ print(msg, "Evaluation will stop.")
+ return
+ # else:
+ # print("="*15)
+ # print("{}s left!".format(OVERALL_TIMEOUT - overall_elapsed))
+
action = _payload['action']
inference_time = _payload['inference_time']
# We record this metric in two keys:
@@ -719,6 +844,7 @@ class FlatlandRemoteEvaluationService:
self.update_running_stats("current_episode_controller_inference_time", inference_time)
self.update_running_stats("controller_inference_time", inference_time)
+ # Perform the step
time_start = time.time()
_observation, all_rewards, done, info = self.env.step(action)
time_diff = time.time() - time_start
@@ -736,29 +862,31 @@ class FlatlandRemoteEvaluationService:
that episode
"""
self.simulation_rewards_normalized[-1] += \
- cumulative_reward / (
+ (cumulative_reward / (
self.env._max_episode_steps *
self.env.get_num_agents()
- )
+ ))
+
+ # We count the number of agents that malfunctioned by checking how many have 1 more steps left before recovery
+ num_malfunctioning = sum(agent.malfunction_data['malfunction'] == 1 for agent in self.env.agents)
- num_malfunctioning = sum(agent.malfunction_data['malfunction'] > 0 for agent in self.env.agents)
- if (num_malfunctioning > 0):
- print(num_malfunctioning, "agent malfunctioning at step", self.current_step)
+ if self.verbose and num_malfunctioning > 0:
+ print("Step {}: {} agents have malfunctioned and will recover next step".format(self.current_step, num_malfunctioning))
self.nb_malfunctioning_trains[-1] += num_malfunctioning
# record the actions before checking for done
- if self.actionDir is not None:
- self.lActions.append(action)
+ if self.action_dir is not None:
+ self.episode_actions.append(action)
- # all done! episode over
+ # Is the episode over?
if done["__all__"]:
self.simulation_done = True
if self.begin_simulation:
# If begin simulation has already been initialized at least once
# This adds the simulation time for the previous episode
- self.simulation_times.append(time.time() - self.begin_simulation)
+ self.simulation_times[-1] = time.time() - self.begin_simulation
# Compute percentage complete
complete = 0
@@ -769,26 +897,44 @@ class FlatlandRemoteEvaluationService:
percentage_complete = complete * 1.0 / self.env.get_num_agents()
self.simulation_percentage_complete[-1] = percentage_complete
- print("Evaluation finished in {} timesteps, {:.3f} seconds. Percentage agents done: {:.3f}. Normalized reward: {:.3f}. Number of malfunctions: {}.".format(
- self.simulation_steps[-1],
- self.simulation_times[-1],
- self.simulation_percentage_complete[-1],
- self.simulation_rewards_normalized[-1],
- self.nb_malfunctioning_trains[-1]
- ))
+ # adds 1.0 so we can add them up
+ self.simulation_rewards_normalized[-1] += 1.0
+
+ if self.current_test not in self.simulation_percentage_complete_per_test:
+ self.simulation_percentage_complete_per_test[self.current_test] = []
+ self.simulation_percentage_complete_per_test[self.current_test].append(percentage_complete)
+ print("Percentage for test {}, level {}: {}".format(self.current_test, self.current_level, percentage_complete))
+
+ if len(self.env.cur_episode) > 0:
+ g3Ep = np.array(self.env.cur_episode)
+ self.nb_deadlocked_trains.append(np.sum(g3Ep[-1, :, 5]))
+ else:
+ self.nb_deadlocked_trains.append(np.nan)
+
+ print(
+ "Evaluation finished in {} timesteps, {:.3f} seconds. Percentage agents done: {:.3f}. Normalized reward: {:.3f}. Number of malfunctions: {}.".format(
+ self.simulation_steps[-1],
+ self.simulation_times[-1],
+ self.simulation_percentage_complete[-1],
+ self.simulation_rewards_normalized[-1],
+ self.nb_malfunctioning_trains[-1],
+ self.nb_deadlocked_trains[-1]
+ ))
+
+ print("Total normalized reward so far: {:.3f}".format(sum(self.simulation_rewards_normalized)))
# Write intermediate results
if self.result_output_path:
self.evaluation_metadata_df.to_csv(self.result_output_path)
print("Wrote intermediate output results to : {}".format(self.result_output_path))
- if self.actionDir is not None:
+ if self.action_dir is not None:
self.save_actions()
- if self.episodeDir is not None:
+ if self.episode_dir is not None:
self.save_episode()
- if self.mergeDir is not None:
+ if self.merge_dir is not None:
self.save_merged_env()
# Record Frame
@@ -816,7 +962,7 @@ class FlatlandRemoteEvaluationService:
def save_actions(self):
sfEnv = self.env_file_paths[self.simulation_count]
- sfActions = self.actionDir + "/" + sfEnv.replace(".pkl", ".json")
+ sfActions = self.action_dir + "/" + sfEnv.replace(".pkl", ".json")
print("env path: ", sfEnv, " sfActions:", sfActions)
@@ -824,20 +970,20 @@ class FlatlandRemoteEvaluationService:
os.makedirs(os.path.dirname(sfActions))
with open(sfActions, "w") as fOut:
- json.dump(self.lActions, fOut)
+ json.dump(self.episode_actions, fOut)
- self.lActions = []
+ self.episode_actions = []
def save_episode(self):
sfEnv = self.env_file_paths[self.simulation_count]
- sfEpisode = self.episodeDir + "/" + sfEnv
+ sfEpisode = self.episode_dir + "/" + sfEnv
print("env path: ", sfEnv, " sfEpisode:", sfEpisode)
RailEnvPersister.save_episode(self.env, sfEpisode)
# self.env.save_episode(sfEpisode)
def save_merged_env(self):
sfEnv = self.env_file_paths[self.simulation_count]
- sfMergeEnv = self.mergeDir + "/" + sfEnv
+ sfMergeEnv = self.merge_dir + "/" + sfEnv
if not os.path.exists(os.path.dirname(sfMergeEnv)):
os.makedirs(os.path.dirname(sfMergeEnv))
@@ -878,14 +1024,14 @@ class FlatlandRemoteEvaluationService:
# Compute the evaluation metadata for the last episode
self.update_evaluation_metadata()
- if len(self.simulation_rewards) != len(self.env_file_paths):
+ if len(self.simulation_rewards) != len(self.env_file_paths) and not self.evaluation_done:
raise Exception(
"""env.submit called before the agent had the chance
to operate on all the test environments.
"""
)
- mean_reward, mean_normalized_reward, mean_percentage_complete = self.compute_mean_scores()
+ mean_reward, mean_normalized_reward, sum_normalized_reward, mean_percentage_complete = self.compute_mean_scores()
if self.visualize and len(os.listdir(self.vizualization_folder_name)) > 0:
# Generate the video
@@ -946,24 +1092,30 @@ class FlatlandRemoteEvaluationService:
#####################################################################
# Update evaluation state
#####################################################################
+
self.evaluation_state["state"] = "FINISHED"
self.evaluation_state["progress"] = 1.0
self.evaluation_state["simulation_count"] = self.simulation_count
- self.evaluation_state["score"]["score"] = mean_percentage_complete
- self.evaluation_state["score"]["score_secondary"] = mean_reward
+ self.evaluation_state["score"]["score"] = sum_normalized_reward
+ self.evaluation_state["score"]["score_secondary"] = mean_percentage_complete
self.evaluation_state["meta"]["normalized_reward"] = mean_normalized_reward
self.evaluation_state["meta"]["reward"] = mean_reward
self.evaluation_state["meta"]["percentage_complete"] = mean_percentage_complete
+ self.evaluation_state["meta"]["termination_cause"] = self.termination_cause
self.handle_aicrowd_success_event(self.evaluation_state)
+
print("#" * 100)
print("EVALUATION COMPLETE !!")
print("#" * 100)
print("# Mean Reward : {}".format(mean_reward))
+ print("# Sum Normalized Reward : {} (primary score)".format(sum_normalized_reward))
+ print("# Mean Percentage Complete : {} (secondary score)".format(mean_percentage_complete))
print("# Mean Normalized Reward : {}".format(mean_normalized_reward))
- print("# Mean Percentage Complete : {}".format(mean_percentage_complete))
print("#" * 100)
print("#" * 100)
+ return _command_response
+
def compute_mean_scores(self):
#################################################################################
#################################################################################
@@ -971,22 +1123,21 @@ class FlatlandRemoteEvaluationService:
# we group all the results by the test_ids
# so we first compute the mean in each of the test_id groups,
# and then we compute the mean across each of the test_id groups
- #
- #
#################################################################################
#################################################################################
source_df = self.evaluation_metadata_df.dropna()
- grouped_df = source_df.groupby(['test_id']).mean()
+ # grouped_df = source_df.groupby(['test_id']).mean()
- mean_reward = grouped_df["reward"].mean()
- mean_normalized_reward = grouped_df["normalized_reward"].mean()
- mean_percentage_complete = grouped_df["percentage_complete"].mean()
+ mean_reward = source_df["reward"].mean()
+ mean_normalized_reward = source_df["normalized_reward"].mean()
+ sum_normalized_reward = source_df["normalized_reward"].sum()
+ mean_percentage_complete = source_df["percentage_complete"].mean()
# Round off the reward values
mean_reward = round(mean_reward, 2)
mean_normalized_reward = round(mean_normalized_reward, 5)
mean_percentage_complete = round(mean_percentage_complete, 3)
- return mean_reward, mean_normalized_reward, mean_percentage_complete
+ return mean_reward, mean_normalized_reward, sum_normalized_reward, mean_percentage_complete
def report_error(self, error_message, command_response_channel):
"""
@@ -996,15 +1147,20 @@ class FlatlandRemoteEvaluationService:
_command_response = {}
_command_response['type'] = messages.FLATLAND_RL.ERROR
_command_response['payload'] = error_message
- _redis.rpush(
- command_response_channel,
- msgpack.packb(
+
+ if self.use_pickle:
+ bytes_error = pickle.dumps(_command_response)
+ else:
+ bytes_error = msgpack.packb(
_command_response,
default=m.encode,
use_bin_type=True)
- )
+
+ _redis.rpush(command_response_channel, bytes_error)
+
self.evaluation_state["state"] = "ERROR"
self.evaluation_state["error"] = error_message
+ self.evaluation_state["meta"]["termination_cause"] = "An error occured."
self.handle_aicrowd_error_event(self.evaluation_state)
def handle_aicrowd_info_event(self, payload):
@@ -1032,36 +1188,53 @@ class FlatlandRemoteEvaluationService:
"""
print("Listening at : ", self.command_channel)
MESSAGE_QUEUE_LATENCY = []
- while True:
+ while True:
try:
command = self.get_next_command()
except timeout_decorator.timeout_decorator.TimeoutError:
# a timeout occurred: send an error, and give -1.0 normalized score for this episode
if self.previous_command['type'] == messages.FLATLAND_RL.ENV_STEP:
self.send_error({"type": messages.FLATLAND_RL.ENV_STEP_TIMEOUT})
+ timeout_details = "step time limit of {}s".format(PER_STEP_TIMEOUT)
elif self.previous_command['type'] == messages.FLATLAND_RL.ENV_CREATE:
self.send_error({"type": messages.FLATLAND_RL.ENV_RESET_TIMEOUT})
+ timeout_details = "pre-planning time limit of {}s".format(INTIAL_PLANNING_TIMEOUT)
self.simulation_steps[-1] += 1
self.simulation_rewards[-1] = self.env._max_episode_steps * self.env.get_num_agents()
- self.simulation_rewards_normalized[-1] = -1.0
-
- print("Evaluation TIMED OUT after {} timesteps, using max penalty. Percentage agents done: {:.3f}. Normalized reward: {:.3f}. Number of malfunctions: {}".format(
- self.simulation_steps[-1],
- self.simulation_percentage_complete[-1],
- self.simulation_rewards_normalized[-1],
- self.nb_malfunctioning_trains[-1],
- ))
+ self.simulation_rewards_normalized[-1] = 0.0
+
+ print(
+ "Evaluation of this episode TIMED OUT after {} timesteps (exceeded {}), won't get any reward. {} consecutive timeouts. "
+ "Percentage agents done: {:.3f}. Normalized reward: {:.3f}. Number of malfunctions: {}.".format(
+ self.simulation_steps[-1],
+ timeout_details,
+ self.timeout_counter,
+ self.simulation_percentage_complete[-1],
+ self.simulation_rewards_normalized[-1],
+ self.nb_malfunctioning_trains[-1],
+ ))
self.timeout_counter += 1
self.state_env_timed_out = True
self.simulation_done = True
- print("Consecutive timeouts: {}".format(self.timeout_counter))
- if self.timeout_counter > MAX_SUCCESSIVE_TIMEOUTS:
- raise Exception("{} consecutive timeouts, aborting.".format(self.timeout_counter))
+ if self.timeout_counter >= MAX_SUCCESSIVE_TIMEOUTS:
+ print("=" * 15)
+ msg = "Submissions had {} consecutive timeouts.".format(self.timeout_counter)
+ print(msg, "Evaluation will stop.")
+ self.termination_cause = msg
+ self.evaluation_done = True
+ # JW - change the command to a submit
+ print("Creating fake submit message after excessive timeouts.")
+ command = {
+ "type": messages.FLATLAND_RL.ENV_SUBMIT,
+ "payload": {},
+ "response_channel": self.previous_command.get("response_channel")}
+
+ return self.handle_env_submit(command)
continue
@@ -1180,10 +1353,10 @@ if __name__ == "__main__":
help="use pickle instead of msgpack",
required=False)
- parser.add_argument('--noShuffle',
+ parser.add_argument('--shuffle',
default=False,
action="store_true",
- help="don't shuffle the envs. Default is to shuffle.",
+ help="Shuffle the environments",
required=False)
parser.add_argument('--disableTimeouts',
@@ -1203,7 +1376,6 @@ if __name__ == "__main__":
help="Results CSV path",
required=False)
-
parser.add_argument('--verbose',
default=False,
action="store_true",
@@ -1220,11 +1392,11 @@ if __name__ == "__main__":
visualize=True,
video_generation_envs=["Test_0/Level_100.pkl"],
result_output_path=args.resultsDir,
- actionDir=args.actionDir,
- episodeDir=args.episodeDir,
- mergeDir=args.mergeDir,
+ action_dir=args.actionDir,
+ episode_dir=args.episodeDir,
+ merge_dir=args.mergeDir,
use_pickle=args.pickle,
- shuffle=not args.noShuffle,
+ shuffle=args.shuffle,
missing_only=args.missingOnly,
disable_timeouts=args.disableTimeouts
)
diff --git a/flatland/utils/env_edit_utils.py b/flatland/utils/env_edit_utils.py
index b1a401740f01c57d4fe4ed86a303b3b45e7945ab..98a22b809d668a9062646c7ca3644b0c35c2092f 100644
--- a/flatland/utils/env_edit_utils.py
+++ b/flatland/utils/env_edit_utils.py
@@ -59,7 +59,8 @@ def makeEnv2(nAg=2, shape=(20,10), llrcPaths=[], lrcStarts=[], lrcTargs=[], liDi
number_of_agents=nAg,
schedule_generator=oSG,
obs_builder_object=obs.TreeObsForRailEnv(max_depth=1),
- close_following=bUCF)
+ close_following=bUCF,
+ record_steps=True)
envModel = editor.EditorModel(env)
env.reset()
@@ -70,9 +71,7 @@ def makeEnv2(nAg=2, shape=(20,10), llrcPaths=[], lrcStarts=[], lrcTargs=[], liDi
return env, envModel
-def makeTestEnv(sName="single_alternative", nAg=2, bUCF=True):
-
- ddEnvSpecs = {
+ddEnvSpecs = {
# opposing stations with single alternative path
"single_alternative":{
"llrcPaths": [
@@ -116,10 +115,34 @@ def makeTestEnv(sName="single_alternative", nAg=2, bUCF=True):
"lrcStarts": [(1,3)],
"lrcTargs": [(2,1)],
"liDirs": [1]
+ },
+
+ # two loops
+ "loop_with_loops": {
+ "llrcPaths": [
+ # big outer loop Row 1, 8; Col 1, 15
+ [(1,1), (1,15), (8, 15), (8,1), (1,1), (1,3)],
+ # alternative 1
+ [(1,3), (1,5), (3,5), (3,10), (1, 10), (1, 12)],
+ # alternative 2
+ [(8,3), (8,5), (6,5), (6,10), (8, 10), (8, 12)],
+
+ ],
+
+ # list of row,col of agent start cells
+ "lrcStarts": [(1,3), (8, 3)],
+ # list of row,col of targets
+ "lrcTargs": [(8,2), (1,2)],
+ # list of initial directions
+ "liDirs": [1, 1],
}
}
+
+def makeTestEnv(sName="single_alternative", nAg=2, bUCF=True):
+ global ddEnvSpecs
+
dSpec = ddEnvSpecs[sName]
return makeEnv2(nAg=nAg, bUCF=bUCF, **dSpec)
diff --git a/flatland/utils/jupyter_utils.py b/flatland/utils/jupyter_utils.py
index 6dd475018bcef6ba92907f61a12f183acc26d660..f28f07af1142d8e7029aa7553d6c7a5c667f46b3 100644
--- a/flatland/utils/jupyter_utils.py
+++ b/flatland/utils/jupyter_utils.py
@@ -11,6 +11,7 @@ from flatland.envs.agent_utils import EnvAgent
from flatland.envs import rail_generators as rail_gen
from flatland.envs import agent_chains as ac
from flatland.envs.rail_env import RailEnv, RailEnvActions
+
from flatland.envs.persistence import RailEnvPersister
from flatland.utils.rendertools import RenderTool
from flatland.utils import env_edit_utils as eeu
@@ -28,6 +29,16 @@ class AlwaysForward(Behaviour):
def getActions(self):
return { i:RailEnvActions.MOVE_FORWARD for i in range(self.nAg) }
+class DelayedStartForward(AlwaysForward):
+ def __init__(self, env, nStartDelay=2):
+ self.nStartDelay = nStartDelay
+ super().__init__(env)
+
+ def getActions(self):
+ iStep = self.env._elapsed_steps + 1
+ nAgentsMoving = min(self.nAg, iStep // self.nStartDelay)
+ return { i:RailEnvActions.MOVE_FORWARD for i in range(nAgentsMoving) }
+
AgentPause = NamedTuple("AgentPause",
[
("iAg", int),
@@ -71,6 +82,27 @@ class ForwardWithPause(Behaviour):
return dAction
+class Deterministic(Behaviour):
+ def __init__(self, env, dAg_lActions):
+ super().__init__(env)
+ self.dAg_lActions = dAg_lActions
+
+ def getActions(self):
+ iStep = self.env._elapsed_steps
+
+ dAg_Action = {}
+ for iAg, lActions in self.dAg_lActions.items():
+ if iStep < len(lActions):
+ iAct = lActions[iStep]
+ else:
+ iAct = RailEnvActions.DO_NOTHING
+ dAg_Action[iAg] = iAct
+ #print(iStep, dAg_Action[0])
+ return dAg_Action
+
+
+
+
class EnvCanvas():
@@ -86,7 +118,7 @@ class EnvCanvas():
self.render()
def render(self):
- self.oRT.render_env(show_rowcols=True, show_inactive_agents=True, show_observations=False)
+ self.oRT.render_env(show_rowcols=True, show_inactive_agents=False, show_observations=False)
self.oCan.put_image_data(self.oRT.get_image())
def step(self):
diff --git a/notebooks/Agent-Close-Following.ipynb b/notebooks/Agent-Close-Following.ipynb
index 6f0d635bb1d8e296197e9327c2da950aa4111271..069052747e91dadea4c1ec0afd9475fe9d2fd9db 100644
--- a/notebooks/Agent-Close-Following.ipynb
+++ b/notebooks/Agent-Close-Following.ipynb
@@ -54,22 +54,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>.container { width:95% !important; }</style>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
@@ -79,19 +66,21 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import networkx as nx\n",
"import PIL\n",
"from IPython import display\n",
- "import time"
+ "import time\n",
+ "from matplotlib import pyplot as plt\n",
+ "import numpy as np"
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -117,679 +106,14 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
- "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
- " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
- "<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n",
- " -->\n",
- "<!-- Pages: 1 -->\n",
- "<svg width=\"537pt\" height=\"1136pt\"\n",
- " viewBox=\"0.00 0.00 537.14 1136.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
- "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 1132)\">\n",
- "<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-1132 533.1419,-1132 533.1419,4 -4,4\"/>\n",
- "<!-- (1, 0) -->\n",
- "<g id=\"node1\" class=\"node\">\n",
- "<title>(1, 0)</title>\n",
- "<ellipse fill=\"none\" stroke=\"#000000\" cx=\"82.5473\" cy=\"-48\" rx=\"30.5947\" ry=\"18\"/>\n",
- "<text text-anchor=\"middle\" x=\"82.5473\" y=\"-44.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">(1, 0)</text>\n",
- "<text text-anchor=\"middle\" x=\"30.5\" y=\"-18.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">stopped</text>\n",
- "<text text-anchor=\"middle\" x=\"30.5\" y=\"-3.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">chain</text>\n",
- "</g>\n",
- "<!-- (1, 1) -->\n",
- "<g id=\"node2\" class=\"node\">\n",
- "<title>(1, 1)</title>\n",
- "<ellipse fill=\"none\" stroke=\"#000000\" cx=\"187.5473\" cy=\"-48\" rx=\"30.5947\" ry=\"18\"/>\n",
- "<text text-anchor=\"middle\" x=\"187.5473\" y=\"-44.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">(1, 1)</text>\n",
- "</g>\n",
- "<!-- (1, 0)->(1, 1) -->\n",
- "<g id=\"edge1\" class=\"edge\">\n",
- "<title>(1, 0)->(1, 1)</title>\n",
- "<path fill=\"none\" stroke=\"#000000\" d=\"M113.1999,-48C123.5669,-48 135.3172,-48 146.3654,-48\"/>\n",
- "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"146.6748,-51.5001 156.6748,-48 146.6748,-44.5001 146.6748,-51.5001\"/>\n",
- "</g>\n",
- "<!-- (1, 2) -->\n",
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- "<!-- (16, 2)->(15, 3) -->\n",
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- "</g>\n",
- "</svg>\n"
- ],
- "text/plain": [
- "<graphviz.files.Source at 0x7fce733f2278>"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"omc = ac.MotionCheck()\n",
"ac.create_test_agents2(omc)\n",
- "ac.render(omc)"
+ "rv = ac.render(omc)\n",
+ "print(type(rv))"
]
},
{
@@ -808,7 +132,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -817,680 +141,63 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
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- "outputs": [
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- ],
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- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"gvDot = ac.render(omc)\n",
"gvDot"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#for v, dPred in omc.G.pred.items():\n",
+ "# print (v, dPred)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#import graphviz"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#oAG = nx.drawing.nx_agraph.to_agraph(omc.G)\n",
+ "#oAG.layout(\"dot\")\n",
+ "#sDot = oAG.to_string()\n",
+ "#oSrc = graphviz.Source(sDot)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#sSVG = oSrc._repr_svg_()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#display.SVG(sSVG)"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -1501,24 +208,9 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "57bd28f57f2245a492518814f3e60e27",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Canvas(layout=Layout(height='300px', width='600px'), size=(600, 300))"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"env, envModel = eeu.makeTestEnv(\"merging_spurs\", nAg=10, bUCF=True)\n",
"oEC = ju.EnvCanvas(env)\n",
@@ -1527,1604 +219,11 @@
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{
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- "</g>\n",
- "</svg>\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"for i in range(10):\n",
" oEC.step()\n",
@@ -3137,7 +236,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -3155,7 +254,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -3164,6 +263,124 @@
" dAgState[iAg] = (*ag.position, ag.direction)\n",
"assert dAgState == dAgStateFrozen\n"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "env.motionCheck.svDeadlocked"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Deadlocking agents\n",
+ "We have added deadlock detection in the close-following code. This detects agents which are head-to-head ie facing in opposing directions in adjacent squares, and thus will not be able to move for the rest of the episode. This deadlocked status is propagated to any other agents which are blocked by the opposing pair.\n",
+ "\n",
+ "In the example below, agents 0 and 1 collide head on. The other agents are spaced out behind them and collide into them in subsequent steps.\n",
+ "\n",
+ "The deadlock status is now recorded in element 5 of each agent in the recorded episode. (row, col, dir, status, malfunction, deadlock)\n",
+ "\n",
+ "__Bugs / Limitations__\n",
+ "\n",
+ "The code does not currently count agents which are deadlocked, if they are also malfunctioning, or choose to stop moving voluntarily.\n",
+ "\n",
+ "The code does not detect agents which are about to become deadlocked, because they are heading towards each other on a track with no junctions or relevant targets."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "env, envModel = eeu.makeTestEnv(\"loop_with_loops\", nAg=10, bUCF=True)\n",
+ "oEC = ju.EnvCanvas(env, behaviour=ju.DelayedStartForward(env, nStartDelay=1))\n",
+ "oEC.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for i in range(25):\n",
+ " oEC.step()\n",
+ " oEC.render()\n",
+ " \n",
+ " #display.display_html(f\"<br>Step: {i}\\n\", raw=True)\n",
+ " #display.display_svg(ac.render(env.motionCheck, horizontal=(i>=3)))\n",
+ " time.sleep(0.1) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "env.motionCheck.svDeadlocked"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "g3Ep = np.array(env.cur_episode)\n",
+ "g3Ep.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nSteps = g3Ep.shape[0]\n",
+ "plt.step(range(nSteps), np.sum(g3Ep[:,:,5], axis=1))\n",
+ "plt.title(\"Deadlocked agents\")\n",
+ "plt.xticks(range(g3Ep.shape[0]))\n",
+ "plt.yticks(range(11))\n",
+ "plt.grid()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "gnDeadlockExpected = np.array([ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 5, 7, 9, 10, 10, 10, 10])\n",
+ "gnDeadlock = np.sum(g3Ep[:,:,5], axis=1)\n",
+ "\n",
+ "assert np.all(gnDeadlock == gnDeadlockExpected), \"Deadlocks by step do not match expected values!\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cmap = plt.get_cmap(\"summer\")\n",
+ "nT, nAg, _ = g3Ep.shape\n",
+ "plt.pcolormesh(g3Ep[:,:,5].T, cmap=cmap)\n",
+ "plt.xlabel(\"Time Step\")\n",
+ "plt.xticks(range(0,nT, 2))\n",
+ "plt.ylabel(\"Agent index\")\n",
+ "plt.yticks(range(nAg))\n",
+ "\n",
+ "\n",
+ "plt.colorbar(ticks=[0,1], fraction=0.018, pad=0.1)\n",
+ "plt.title(\"Deadlock status\")\n",
+ "plt.grid()"
+ ]
}
],
"metadata": {
@@ -3244,6 +461,88 @@
"_Feature"
],
"window_display": false
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+ "state": {
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+ "_model_module": "ipycanvas",
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+ "_view_module": "ipycanvas",
+ "_view_module_version": "^0.4.1",
+ "_view_name": "CanvasView",
+ "image_data": null,
+ "layout": "IPY_MODEL_c1b255e12efc45308a00213767bc1ed0",
+ "size": [
+ 600,
+ 300
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+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": "300px",
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+ "justify_items": null,
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diff --git a/notebooks/flatland-animate.ipynb b/notebooks/flatland-animate.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..6f92a90b12de9a6e37d858c2e4ad18f144cfa854
--- /dev/null
+++ b/notebooks/flatland-animate.ipynb
@@ -0,0 +1,226 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Simple Animation Demo"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%load_ext autoreload\n",
+ "%autoreload 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from flatland.utils.rendertools import RenderTool\n",
+ "import ipycanvas\n",
+ "import time\n",
+ "from flatland.envs.rail_env import RailEnv\n",
+ "from flatland.envs.rail_env import RailEnvActions as rea\n",
+ "from flatland.envs.persistence import RailEnvPersister"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "env, env_dict = RailEnvPersister.load_new(\"test_env_loop.pkl\", load_from_package=\"env_data.tests\")\n",
+ "oRT = RenderTool(env)\n",
+ "oRT.render_env()\n",
+ "image_arr = oRT.get_image()\n",
+ "canvas = ipycanvas.Canvas()\n",
+ "canvas.put_image_data(image_arr)\n",
+ "display(canvas)\n",
+ "\n",
+ "for iEp in range(5):\n",
+ " env.reset(False, False, True)\n",
+ " env._max_episode_steps = 100\n",
+ "\n",
+ " done={\"__all__\":False}\n",
+ " while not done[\"__all__\"]:\n",
+ " obs, rew, done, info = env.step(action_dict_={int(0):rea.MOVE_FORWARD})\n",
+ " oRT.render_env()\n",
+ " gIm = oRT.get_image()\n",
+ " canvas.put_image_data(gIm)\n",
+ " time.sleep(0.1)"
+ ]
+ }
+ ],
+ "metadata": {
+ "hide_input": false,
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
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+ },
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+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
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+ "hotkeys": {
+ "equation": "Ctrl-E",
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+ "nav_menu": {},
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+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
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+ "delete_cmd_prefix": "rm(",
+ "library": "var_list.r",
+ "varRefreshCmd": "cat(var_dic_list()) "
+ }
+ },
+ "types_to_exclude": [
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+ "_view_name": "CanvasView",
+ "image_data": null,
+ "layout": "IPY_MODEL_2433ff4d1b324fac968f6109f0a61185",
+ "size": [
+ 700,
+ 500
+ ],
+ "sync_image_data": false
+ }
+ }
+ },
+ "version_major": 2,
+ "version_minor": 0
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/notebook-list b/notebooks/notebook-list
new file mode 100644
index 0000000000000000000000000000000000000000..794db00689f4154c3d1b3148add85492adffa3ba
--- /dev/null
+++ b/notebooks/notebook-list
@@ -0,0 +1,12 @@
+# list of notebooks to include in run-all-notebooks.py test
+Agent-Close-Following.ipynb
+flatland-animate.ipynb
+render-episode.ipynb
+Scene_Editor.ipynb
+simple_example1_env_from_tuple.ipynb
+simple_example2_generate_random_rail.ipynb
+simple_example_3_manual_control.ipynb
+Simple_Rendering_Demo.ipynb
+test-collision.ipynb
+test-saved-envs.ipynb
+test-service.ipynb
diff --git a/notebooks/render-episode.ipynb b/notebooks/render-episode.ipynb
index 3068d46d8daa155219c16ae497144879f5429675..d663c639ec42f4ea98b1d1669e827d02e03715b4 100644
--- a/notebooks/render-episode.ipynb
+++ b/notebooks/render-episode.ipynb
@@ -1,5 +1,16 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Render Episode\n",
+ "Render a stored episode. Env file needs to have \"episode\" and \"action\" keys.\n",
+ "- checks that the agent actions match the saved steps (row, col, dir)\n",
+ "- creates a moving gif file of the episode\n",
+ "- displays the episode in a widget with a slider for the time steps."
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {
@@ -105,7 +116,9 @@
"id": "UeX1h4c0i5e6"
},
"source": [
- "# Experiments"
+ "# Experiments\n",
+ "\n",
+ "This has been mostly changed to load envs using `importlib_resources`. It's getting them from the package \"envdata.tests`"
]
},
{
@@ -130,30 +143,30 @@
"metadata": {},
"outputs": [],
"source": [
- "if os.path.exists(\"../env_data\"):\n",
- " env_file = \"../env_data/tests/Test_2_Level_0.pkl\"\n",
- "else:\n",
- " env_file = \"./env_data/tests/Test_2_Level_0.pkl\""
+ "#if os.path.exists(\"../env_data\"):\n",
+ "# env_file = \"../env_data/tests/Test_2_Level_0.pkl\"\n",
+ "#else:\n",
+ "# env_file = \"./env_data/tests/Test_2_Level_0.pkl\""
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "colab": {},
- "colab_type": "code",
- "id": "PU5GkH271guD"
- },
+ "metadata": {},
"outputs": [],
"source": [
- "import pickle\n",
- "\n",
- "from flatland.envs.rail_env import RailEnv\n",
- "from flatland.envs.rail_generators import sparse_rail_generator\n",
- "from flatland.envs.schedule_generators import sparse_schedule_generator\n",
- "from flatland.envs.malfunction_generators import malfunction_from_file, no_malfunction_generator\n",
- "from flatland.envs.rail_generators import rail_from_file\n",
- "from flatland.envs.schedule_generators import schedule_from_file"
+ "#sPack, sResource = \"env_data.tests\", \"Test_2_Level_0.pkl\"\n",
+ "sPack, sResource = \"env_data.tests\", \"Test_9_Level_1.pkl\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#env_file = \"../../evaluation_visualization/round2/or-0827/Test_23/Level_1.pkl\"\n",
+ "#env_file = \"../../evaluation_visualization/round2/rl-0827/Test_23/Level_1.pkl\""
]
},
{
@@ -166,27 +179,14 @@
},
"outputs": [],
"source": [
- "with open(env_file, \"rb\") as fIn:\n",
- " env_dict = pickle.load(fIn)\n",
- "\n",
- "def make_env():\n",
- " env = RailEnv(\n",
- " width=1, height=1,\n",
- " rail_generator=rail_from_file(env_file),\n",
- " schedule_generator=schedule_from_file(env_file),\n",
- " malfunction_generator_and_process_data=malfunction_from_file(env_file),\n",
- " #malfunction_generator_and_process_data=no_malfunction_generator,\n",
- " close_following = False,\n",
- " )\n",
- "\n",
- " obs, info = env.reset(\n",
- " regenerate_rail=True,\n",
- " regenerate_schedule=True,\n",
- " activate_agents=False,\n",
- " random_seed=1001\n",
- " )\n",
+ "import pickle\n",
"\n",
- " return env, obs, info"
+ "from flatland.envs.rail_env import RailEnv\n",
+ "from flatland.envs.rail_generators import sparse_rail_generator\n",
+ "from flatland.envs.schedule_generators import sparse_schedule_generator\n",
+ "from flatland.envs.malfunction_generators import malfunction_from_file, no_malfunction_generator\n",
+ "from flatland.envs.rail_generators import rail_from_file\n",
+ "from flatland.envs.schedule_generators import schedule_from_file"
]
},
{
@@ -204,10 +204,11 @@
"metadata": {},
"outputs": [],
"source": [
- "env, env_dict = RailEnvPersister.load_new(env_file)\n",
+ "env, env_dict = RailEnvPersister.load_new(sResource, load_from_package=sPack) # env_file)\n",
+ "\n",
+ "# the seed has to match that used to record the episode, in order for the malfunctions to match.\n",
"env.reset(random_seed=1001)\n",
"oRT = RenderTool(env, show_debug=True)\n",
- "\n",
"aImg = oRT.render_env(show_rowcols=True, return_image=True, show_inactive_agents=True)\n",
"print(env._max_episode_steps)\n",
"PIL.Image.fromarray(aImg)\n"
@@ -330,8 +331,9 @@
"\n",
"step = 0\n",
"all_done = False\n",
+ "failed_action_check = False\n",
"print(\"Processing episode steps:\")\n",
- "while not all_done and step < max_steps:\n",
+ "while not all_done and step < len(expert_actions):\n",
" print(step, end=\", \")\n",
" \"\"\"\n",
" for a in range(n_agents):\n",
@@ -347,10 +349,12 @@
" action_dict.update({a: action})\n",
" \"\"\"\n",
" \n",
- " dAct = expert_actions[step]\n",
- " #print(dAct)\n",
" if step < len(expert_actions):\n",
- " next_obs, all_rewards, done, info = env.step(expert_actions[step])\n",
+ " dAct = expert_actions[step]\n",
+ " else:\n",
+ " dAct = {}\n",
+ " \n",
+ " next_obs, all_rewards, done, info = env.step(dAct)\n",
" \n",
" if True:\n",
" # Check that agent states match recorded states\n",
@@ -358,6 +362,7 @@
" pass\n",
" else:\n",
" print(\"MISMATCH\")\n",
+ " failed_action_check = True\n",
" #print(\"env:\", get_agent_state(env))\n",
" #print(\"epi:\", episode_states[step])\n",
" llAgSt = get_agent_state(env)\n",
@@ -405,6 +410,15 @@
" step += 1"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "assert failed_action_check == False, \"Realised states did not match stored states.\""
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -449,7 +463,7 @@
"metadata": {},
"outputs": [],
"source": [
- "sfImg = env_file.replace(\"pkl\", \"gif\")\n",
+ "sfImg = sResource.replace(\"pkl\", \"gif\")\n",
"imageio.mimsave(sfImg, [d[\"image\"] for d in frames], subrectangles=True)"
]
},
@@ -558,7 +572,39 @@
"metadata": {},
"outputs": [],
"source": [
- "plt.plot(np.sum(g3Ep[:,:,3]>0, axis=1))"
+ "plt.plot(np.sum(g3Ep[:,:,3]>0, axis=1))\n",
+ "plt.title(sResource + \"\\nmalfunctioning agents by time step\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "g3Ep.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "g3Ep2 = np.array(env.cur_episode)\n",
+ "g3Ep2.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.step(range(len(g3Ep2)), np.sum(g3Ep2[:,:,4]==1, axis=1), label=\"Active\")\n",
+ "plt.step(range(len(g3Ep2)), np.sum(g3Ep2[:,:,4]==0, axis=1), label=\"Ready to depart\")\n",
+ "plt.title(\"env: \"+ sResource +\"\\nActive Agents by timestep\")\n",
+ "plt.legend()"
]
},
{
@@ -655,419 +701,418 @@
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
- "0a2d6b655cc6486bba4cb2ed086b2a19": {
- "model_module": "@jupyter-widgets/base",
- "model_name": "LayoutModel",
- "state": {
- "_model_module": "@jupyter-widgets/base",
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- "_model_name": "LayoutModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "LayoutView",
- "align_content": null,
- "align_items": null,
- "align_self": null,
- "border": null,
- "bottom": null,
- "display": null,
- "flex": null,
- "flex_flow": null,
- "grid_area": null,
- "grid_auto_columns": null,
- "grid_auto_flow": null,
- "grid_auto_rows": null,
- "grid_column": null,
- "grid_gap": null,
- "grid_row": null,
- "grid_template_areas": null,
- "grid_template_columns": null,
- "grid_template_rows": null,
- "height": null,
- "justify_content": null,
- "justify_items": null,
- "left": null,
- "margin": null,
- "max_height": null,
- "max_width": null,
- "min_height": null,
- "min_width": null,
- "object_fit": null,
- "object_position": null,
- "order": null,
- "overflow": null,
- "overflow_x": null,
- "overflow_y": null,
- "padding": null,
- "right": null,
- "top": null,
- "visibility": null,
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- "_model_module_version": "1.5.0",
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- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "VBoxView",
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- "IPY_MODEL_711e232d2cb54266a52552d90ce3f05e",
- "IPY_MODEL_176dbef2d38244bcb256258a692d53a7"
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- "_view_name": "OutputView",
- "layout": "IPY_MODEL_0a2d6b655cc6486bba4cb2ed086b2a19",
- "msg_id": "",
- "outputs": [
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}
},
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- }
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+ "version_major": 2,
+ "version_minor": 0
}
}
},
diff --git a/notebooks/run_all_notebooks.py b/notebooks/run_all_notebooks.py
index 6facfa1a2f10f03241fe091a282e3c72e5d0412d..20f6e11907a2f09abd3b402788fa7ef9591a223f 100644
--- a/notebooks/run_all_notebooks.py
+++ b/notebooks/run_all_notebooks.py
@@ -5,6 +5,7 @@ from subprocess import Popen, PIPE
import importlib_resources
import pkg_resources
from importlib_resources import path
+import importlib_resources as ir
from ipython_genutils.py3compat import string_types, bytes_to_str
@@ -38,17 +39,38 @@ def run_python(parameters, ignore_return_code=False, stdin=None):
return stdout.decode('utf8', 'replace'), stderr.decode('utf8', 'replace')
-for entry in [entry for entry in importlib_resources.contents('notebooks') if
- not pkg_resources.resource_isdir('notebooks', entry)
- and entry.endswith(".ipynb")
- ]:
- print("*****************************************************************")
- print("Converting and running {}".format(entry))
- print("*****************************************************************")
-
- with path('notebooks', entry) as file_in:
- out, err = run_python(" -m jupyter nbconvert --execute --to notebook --inplace " + str(file_in))
- sys.stderr.write(err)
- sys.stderr.flush()
- sys.stdout.write(out)
- sys.stdout.flush()
+def main():
+
+ # If the file notebooks-list exists, use it as a definitive list of notebooks to run
+ # This in effect ignores any local notebooks you might be working on, so you can run tox
+ # without them causing the notebooks task / testenv to fail.
+ if importlib_resources.is_resource("notebooks", "notebook-list"):
+ print("Using the notebooks-list file to designate which notebooks to run")
+ lsNB = [
+ sLine for sLine in ir.read_text("notebooks", "notebook-list").split("\n")
+ if len(sLine) > 3 and not sLine.startswith("#")
+ ]
+ else:
+ lsNB = [
+ entry for entry in importlib_resources.contents('notebooks') if
+ not pkg_resources.resource_isdir('notebooks', entry)
+ and entry.endswith(".ipynb")
+ ]
+
+ print("Running notebooks:", " ".join(lsNB))
+
+ for entry in lsNB:
+ print("*****************************************************************")
+ print("Converting and running {}".format(entry))
+ print("*****************************************************************")
+
+ with path('notebooks', entry) as file_in:
+ out, err = run_python(" -m jupyter nbconvert --ExecutePreprocessor.timeout=120 " +
+ "--execute --to notebook --inplace " + str(file_in))
+ sys.stderr.write(err)
+ sys.stderr.flush()
+ sys.stdout.write(out)
+ sys.stdout.flush()
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/notebooks/test-collision.ipynb b/notebooks/test-collision.ipynb
index e72b3b4264f62a212e1afd82eec438e345a9292d..60a21092f00d49ee306d88644aad5531cf334bd3 100644
--- a/notebooks/test-collision.ipynb
+++ b/notebooks/test-collision.ipynb
@@ -10,22 +10,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>.container { width:95% !important; }</style>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
@@ -35,7 +22,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -47,7 +34,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -65,27 +52,12 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "a21b1fd47f49490c97b8972281a325f4",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Canvas(layout=Layout(height='300px', width='600px'), size=(600, 300))"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
- "env, envModel = eeu.makeTestEnv(\"merging_spurs\", nAg=2, bUCF=True)\n",
- "behaviour = ju.ForwardWithPause(env, [ju.AgentPause(0, 10, 5)])\n",
+ "env, envModel = eeu.makeTestEnv(\"concentric_loops\", nAg=2, bUCF=True)\n",
+ "behaviour = ju.ForwardWithPause(env, [ju.AgentPause(0, 12, 5)])\n",
"oEC = ju.EnvCanvas(env, behaviour)\n",
"env.reset(regenerate_rail=False)\n",
"oEC.show()\n",
@@ -97,14 +69,18 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "dAgStateExpected = {0: (7, 15, 2), 1: (6, 15, 2)}\n",
+ "dAgStateExpected = {0: (8, 5, 3), 1: (8, 6, 3)} \n",
+ "# {0: (7, 15, 2), 1: (6, 15, 2)}\n",
+ "\n",
"dAgState={}\n",
"for iAg, ag in enumerate(env.agents):\n",
" dAgState[iAg] = (*ag.position, ag.direction)\n",
+ "print(\"Expected:\", dAgStateExpected)\n",
+ "print(\"Actual:\", dAgState)\n",
"assert dAgState == dAgStateExpected"
]
}
@@ -187,6 +163,88 @@
"_Feature"
],
"window_display": false
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "state": {
+ "0f519ff851d642d6a67cbc7f2b76c6ef": {
+ "model_module": "ipycanvas",
+ "model_module_version": "^0.4.1",
+ "model_name": "CanvasModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "ipycanvas",
+ "_model_module_version": "^0.4.1",
+ "_model_name": "CanvasModel",
+ "_view_count": null,
+ "_view_module": "ipycanvas",
+ "_view_module_version": "^0.4.1",
+ "_view_name": "CanvasView",
+ "image_data": null,
+ "layout": "IPY_MODEL_24409cd7812a436582ba0ac1c4ece5ee",
+ "size": [
+ 600,
+ 300
+ ],
+ "sync_image_data": false
+ }
+ },
+ "24409cd7812a436582ba0ac1c4ece5ee": {
+ "model_module": "@jupyter-widgets/base",
+ "model_module_version": "1.2.0",
+ "model_name": "LayoutModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": "300px",
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": "600px"
+ }
+ }
+ },
+ "version_major": 2,
+ "version_minor": 0
+ }
}
},
"nbformat": 4,
diff --git a/notebooks/test-saved-envs.ipynb b/notebooks/test-saved-envs.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..aeb1fbbea2a1f09f8c936a47d6aa8a2c6895b875
--- /dev/null
+++ b/notebooks/test-saved-envs.ipynb
@@ -0,0 +1,214 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Load some (old) env files to check they work\n",
+ "This notebook just loads some old env files, renders them, and runs a few steps.\n",
+ "This is just a sanity check that these old envs will still load.\n",
+ "Many of them use deprecated data formats so it's just so that we can avoid deleting them for now, and so new participants are not confused by us shipping env files which don't work..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%load_ext autoreload\n",
+ "%autoreload 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from IPython import display\n",
+ "display.display(display.HTML(\"<style>.container { width:95% !important; }</style>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import PIL\n",
+ "import glob\n",
+ "import pickle\n",
+ "import msgpack\n",
+ "import importlib_resources as ir"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from flatland.envs.persistence import RailEnvPersister"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from flatland.utils import env_edit_utils as eeu\n",
+ "from flatland.utils import jupyter_utils as ju"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#lsDirs = [ \"../env_data/railway\", \"../env_data/tests\"]\n",
+ "lsPackages = [ \"env_data.railway\", \"env_data.tests\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ltPackRes = []\n",
+ "for sPack in lsPackages:\n",
+ " for sExt in [\"mpk\", \"pkl\"]:\n",
+ " #lsFiles += glob.glob(sDir + \"/*\" + sExt)\n",
+ " ltPackRes += [ (sPack, sRes) for sRes in ir.contents(sPack) if sRes.endswith(sExt) ]\n",
+ "ltPackRes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if False:\n",
+ " for sFile in lsFiles:\n",
+ " try:\n",
+ " with open(sFile, \"rb\") as fIn:\n",
+ " env_dict = pickle.load(fIn)\n",
+ " print(\"pickle:\", sFile)\n",
+ " except ValueError as oErr:\n",
+ " try:\n",
+ " with open(sFile, \"rb\") as fIn:\n",
+ " env_dict = msgpack.load(fIn)\n",
+ " print(\"msgpack: \", sFile)\n",
+ " except ValueError as oErr:\n",
+ " print(\"msgpack failed: \", sFile)\n",
+ " \n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "for sPack, sRes in ltPackRes:\n",
+ " print(\"Loading: \", sPack, sRes)\n",
+ " env, env_dict = RailEnvPersister.load_new(sRes, load_from_package=sPack)\n",
+ " env.reset()\n",
+ " oCanvas = ju.EnvCanvas(env, ju.AlwaysForward(env))\n",
+ " oCanvas.show()\n",
+ " for iStep in range(10):\n",
+ " oCanvas.step()\n",
+ " oCanvas.render()"
+ ]
+ }
+ ],
+ "metadata": {
+ "hide_input": false,
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.7"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 1,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
+ },
+ "varInspector": {
+ "cols": {
+ "lenName": 16,
+ "lenType": 16,
+ "lenVar": 40
+ },
+ "kernels_config": {
+ "python": {
+ "delete_cmd_postfix": "",
+ "delete_cmd_prefix": "del ",
+ "library": "var_list.py",
+ "varRefreshCmd": "print(var_dic_list())"
+ },
+ "r": {
+ "delete_cmd_postfix": ") ",
+ "delete_cmd_prefix": "rm(",
+ "library": "var_list.r",
+ "varRefreshCmd": "cat(var_dic_list()) "
+ }
+ },
+ "types_to_exclude": [
+ "module",
+ "function",
+ "builtin_function_or_method",
+ "instance",
+ "_Feature"
+ ],
+ "window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/test-service-timeouts.ipynb b/notebooks/test-service-timeouts.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..67a849d8e8df06c6fa0b4dc253aa0ebe553aa4d9
--- /dev/null
+++ b/notebooks/test-service-timeouts.ipynb
@@ -0,0 +1,841 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Test Service\n",
+ "\n",
+ "Intended to test the service.py evaluator.\n",
+ "Runs the service.py and a simple client."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "baXcVq3ii0Cb"
+ },
+ "source": [
+ "Setup\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%load_ext autoreload\n",
+ "%autoreload 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import PIL\n",
+ "from flatland.utils.rendertools import RenderTool\n",
+ "import imageio\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "PU5GkH271guD"
+ },
+ "outputs": [],
+ "source": [
+ "from flatland.envs.rail_env import RailEnv\n",
+ "from flatland.envs.rail_generators import sparse_rail_generator\n",
+ "from flatland.envs.schedule_generators import sparse_schedule_generator\n",
+ "from flatland.envs.malfunction_generators import malfunction_from_file, no_malfunction_generator\n",
+ "from flatland.envs.rail_generators import rail_from_file\n",
+ "from flatland.envs.schedule_generators import schedule_from_file\n",
+ "from flatland.core.env_observation_builder import DummyObservationBuilder\n",
+ "from flatland.envs.persistence import RailEnvPersister\n",
+ "from flatland.evaluators.client import FlatlandRemoteClient, TimeoutException\n",
+ "import flatland.evaluators.service as fes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "PU5GkH271guD"
+ },
+ "outputs": [],
+ "source": [
+ "import pickle\n",
+ "import redis\n",
+ "import subprocess as sp\n",
+ "import shlex\n",
+ "import time\n",
+ "import pkg_resources as pr\n",
+ "import importlib_resources as ir\n",
+ "import sys, os\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sDirRoot = \"/Users/flaurent/Sites/flatland/neurips2020-flatland-starter-kit/scratch/test-neurips2020-round2-v0\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!ps -ef | grep -i python | grep -i flatland.evaluators.service"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def kill_evaluator():\n",
+ " # kill previous evaluator\n",
+ " !ps -ef | grep -i python | grep -i flatland.evaluators.service | awk '{print $2}' | xargs kill"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def cleanup():\n",
+ " oRedis = redis.Redis()\n",
+ " lKeys = oRedis.keys(\"flatland*\")\n",
+ " for sKey in lKeys:\n",
+ " print(\"Deleting:\", sKey)\n",
+ " oRedis.delete(sKey)\n",
+ " \n",
+ " !rm -f /tmp/output.csv\n",
+ " \n",
+ " kill_evaluator()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def configure_env(overall=8*60*60, planning=5*60, step=10, successive=10, mean_percentage=0.25):\n",
+ " osEnv2 = os.environ.copy()\n",
+ " osEnv2[\"FLATLAND_OVERALL_TIMEOUT\"]=str(overall)\n",
+ " osEnv2[\"FLATLAND_PER_STEP_TIMEOUT\"] = str(step)\n",
+ " osEnv2[\"FLATLAND_INITIAL_PLANNING_TIMEOUT\"] = str(planning)\n",
+ " osEnv2[\"FLATLAND_MAX_SUCCESSIVE_TIMEOUTS\"] = str(successive)\n",
+ " osEnv2[\"TEST_MIN_PERCENTAGE_COMPLETE_MEAN\"] = str(mean_percentage)\n",
+ " return osEnv2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def start_evaluator(env):\n",
+ " sCmd = f\"python -m flatland.evaluators.service --test_folder {sDirRoot} --pickle\" # --verbose\"\n",
+ " lsCmd = shlex.split(sCmd)\n",
+ " print(sCmd)\n",
+ " \n",
+ " oPipe = sp.Popen(lsCmd, env=env)\n",
+ " oPipe.poll()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def start_client():\n",
+ " oFRC = FlatlandRemoteClient(test_envs_root=sDirRoot, verbose=False, use_pickle=True)\n",
+ " env, env_dict = RailEnvPersister.load_new(f\"{sDirRoot}/Test_0/Level_0.pkl\")\n",
+ " return oFRC"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def random_controller(obs, _env):\n",
+ " np.random.seed(0)\n",
+ " \n",
+ " dAct = {}\n",
+ " for iAg in range(len(_env.agents)):\n",
+ " dAct[iAg] = np.random.randint(0, 5)\n",
+ " return dAct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def run_submission(oFRC, slow_ep=-1, debug=False):\n",
+ " def log(txt, end=\"\\n\"):\n",
+ " if debug: print(txt, end)\n",
+ " \n",
+ " dummy_obs = DummyObservationBuilder()\n",
+ " episode = 0\n",
+ " obs = True\n",
+ " \n",
+ " while obs:\n",
+ " obs, info = oFRC.env_create(obs_builder_object=dummy_obs)\n",
+ " log(oFRC.current_env_path)\n",
+ " log(f\"Episode : {episode}\")\n",
+ " \n",
+ " if not obs:\n",
+ " log(\"None observation - all envs completed!\")\n",
+ " break\n",
+ " \n",
+ " while True:\n",
+ " action = random_controller(obs, oFRC.env)\n",
+ " \n",
+ " if slow_ep != -1:\n",
+ " # make a specific episode artificially slow\n",
+ " if (episode == slow_ep) and (oFRC.env._elapsed_steps > 10):\n",
+ " time.sleep(2)\n",
+ " \n",
+ " try:\n",
+ " observation, all_rewards, done, info = oFRC.env_step(action)\n",
+ " #log(\".\", end=\"\")\n",
+ " if done['__all__']:\n",
+ " log(\"\\nCompleted Episode : \", episode)\n",
+ " log(\"Reward : \", sum(list(all_rewards.values())))\n",
+ " break\n",
+ " except TimeoutException as err:\n",
+ " log(\"Timeout: \", err)\n",
+ " break\n",
+ " \n",
+ " episode += 1\n",
+ " \n",
+ " #print(f\"Evaluation Complete - episodes={episode} - send submit message...\")\n",
+ " #print(oFRC.submit())\n",
+ " #print(\"All done.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Tests\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%time\n",
+ "\n",
+ "# Normal submission\n",
+ "cleanup()\n",
+ "config = configure_env(overall=8*60*60, planning=5*60, step=10, successive=10, mean_percentage=0.1)\n",
+ "start_evaluator(config)\n",
+ "client = start_client()\n",
+ "run_submission(client)\n",
+ "\n",
+ "#try:\n",
+ "# run_submission(client)\n",
+ "#except Exception as e:\n",
+ "# print(\"== EXCEPTION! ===\")\n",
+ "# print(e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%time\n",
+ "\n",
+ "# Overall timeout\n",
+ "cleanup()\n",
+ "config = configure_env(overall=3, planning=5*60, step=10, successive=10, mean_percentage=0.0)\n",
+ "start_evaluator(config)\n",
+ "client = start_client()\n",
+ "run_submission(client, debug=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%time\n",
+ "\n",
+ "# Step timeout\n",
+ "# FIXME fails!\n",
+ "\"\"\"\n",
+ "cleanup()\n",
+ "config = configure_env(overall=3, planning=5*60, step=10, successive=10, mean_percentage=0.0)\n",
+ "start_evaluator(config)\n",
+ "client = start_client()\n",
+ "run_submission(client, slow_ep=2, debug=True)\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#pd.read_csv(\"/tmp/output.csv\").T"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Cleanup\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "kill_evaluator()"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "collapsed_sections": [],
+ "name": "Flatland Round 2 Replays",
+ "provenance": []
+ },
+ "hide_input": false,
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.8"
+ },
+ "latex_envs": {
+ "LaTeX_envs_menu_present": true,
+ "autoclose": false,
+ "autocomplete": true,
+ "bibliofile": "biblio.bib",
+ "cite_by": "apalike",
+ "current_citInitial": 1,
+ "eqLabelWithNumbers": true,
+ "eqNumInitial": 1,
+ "hotkeys": {
+ "equation": "Ctrl-E",
+ "itemize": "Ctrl-I"
+ },
+ "labels_anchors": false,
+ "latex_user_defs": false,
+ "report_style_numbering": false,
+ "user_envs_cfg": false
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
+ },
+ "varInspector": {
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+ "delete_cmd_postfix": "",
+ "delete_cmd_prefix": "del ",
+ "library": "var_list.py",
+ "varRefreshCmd": "print(var_dic_list())"
+ },
+ "r": {
+ "delete_cmd_postfix": ") ",
+ "delete_cmd_prefix": "rm(",
+ "library": "var_list.r",
+ "varRefreshCmd": "cat(var_dic_list()) "
+ }
+ },
+ "types_to_exclude": [
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diff --git a/notebooks/test-service.ipynb b/notebooks/test-service.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..821cb63a2c48e7d34795be0679406e0606a9a682
--- /dev/null
+++ b/notebooks/test-service.ipynb
@@ -0,0 +1,1292 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Test Service\n",
+ "\n",
+ "Intended to test the service.py evaluator.\n",
+ "Runs the service.py and a simple client.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "baXcVq3ii0Cb"
+ },
+ "source": [
+ "# Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%load_ext autoreload\n",
+ "%autoreload 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "eSHpLxdt1jmE"
+ },
+ "outputs": [],
+ "source": [
+ "import PIL\n",
+ "from flatland.utils.rendertools import RenderTool\n",
+ "import imageio\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "PU5GkH271guD"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>.container { width:95% !important; }</style>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from IPython.display import clear_output\n",
+ "from IPython.core import display\n",
+ "import ipywidgets as ipw\n",
+ "display.display(display.HTML(\"<style>.container { width:95% !important; }</style>\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "PU5GkH271guD"
+ },
+ "outputs": [],
+ "source": [
+ "from flatland.envs.rail_env import RailEnv\n",
+ "from flatland.envs.rail_generators import sparse_rail_generator\n",
+ "from flatland.envs.schedule_generators import sparse_schedule_generator\n",
+ "from flatland.envs.malfunction_generators import malfunction_from_file, no_malfunction_generator\n",
+ "from flatland.envs.rail_generators import rail_from_file\n",
+ "from flatland.envs.schedule_generators import schedule_from_file\n",
+ "from flatland.core.env_observation_builder import DummyObservationBuilder\n",
+ "from flatland.envs.persistence import RailEnvPersister\n",
+ "from flatland.evaluators.client import FlatlandRemoteClient, TimeoutException\n",
+ "import flatland.evaluators.service as fes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "PU5GkH271guD"
+ },
+ "outputs": [],
+ "source": [
+ "import pickle\n",
+ "import redis\n",
+ "import subprocess as sp\n",
+ "import shlex\n",
+ "import time\n",
+ "import pkg_resources as pr\n",
+ "import importlib_resources as ir\n",
+ "import sys, os\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/home/jeremy/projects/aicrowd/rl-trains/flatland5/notebooks\r\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pwd"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Find the real path of the `env_data` package (should be copied by tox)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'pathlib.PosixPath'> /home3/jeremy/projects/aicrowd/rl-trains/flatland5/env_data/tests/test_001.pkl\n"
+ ]
+ }
+ ],
+ "source": [
+ "with ir.path(\"env_data.tests\", \"test_001.pkl\") as oPath:\n",
+ " sPath = oPath\n",
+ "print(type(sPath), sPath)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'/home3/jeremy/projects/aicrowd/rl-trains/flatland5/env_data/tests/service_test/'"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sDirRoot = \"/\" + \"/\".join(sPath.parts[1:-1] + (\"service_test\",\"\"))\n",
+ "sDirRoot"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Clear any old redis keys"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "oRedis = redis.Redis()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[b'flatland-rl::FLATLAND_RL_SERVICE_ID::response::9233d209716f4ae78a5dbe124de67e27']"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "lKeys = oRedis.keys(\"flatland*\")\n",
+ "lKeys"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Deleting: b'flatland-rl::FLATLAND_RL_SERVICE_ID::response::9233d209716f4ae78a5dbe124de67e27'\n"
+ ]
+ }
+ ],
+ "source": [
+ "for sKey in lKeys:\n",
+ " print(\"Deleting:\", sKey)\n",
+ " oRedis.delete(sKey)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Remove `/tmp/output.csv`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!rm -f /tmp/output.csv"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### kill any old `service.py` process"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!ps -ef | grep -i python | grep -i flatland.evaluators.service | awk '{print $2}' | xargs kill"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "osEnv2 = os.environ.copy()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Timeouts copied from service.py"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#MAX_SUCCESSIVE_TIMEOUTS = int(os.getenv(\"FLATLAND_MAX_SUCCESSIVE_TIMEOUTS\", 10))\n",
+ "\n",
+ "# 8 hours (will get debug timeout from env variable if applicable)\n",
+ "#OVERALL_TIMEOUT = int(os.getenv(\n",
+ "# \"FLATLAND_OVERALL_TIMEOUT\",\n",
+ "# 8 * 60 * 60))\n",
+ "\n",
+ "# 10 mins\n",
+ "#INTIAL_PLANNING_TIMEOUT = int(os.getenv(\n",
+ "# \"FLATLAND_INITIAL_PLANNING_TIMEOUT\",\n",
+ "# 10 * 60))\n",
+ "\n",
+ "# 10 seconds\n",
+ "#PER_STEP_TIMEOUT = int(os.getenv(\n",
+ "# \"FLATLAND_PER_STEP_TIMEOUT\",\n",
+ "# 10))\n",
+ "\n",
+ "# 5 min - applies to the rest of the commands\n",
+ "#DEFAULT_COMMAND_TIMEOUT = int(os.getenv(\n",
+ "# \"FLATLAND_DEFAULT_COMMAND_TIMEOUT\",\n",
+ "# 5 * 60))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Set some short timeouts for testing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "osEnv2[\"FLATLAND_OVERALL_TIMEOUT\"]=\"10\"\n",
+ "osEnv2[\"FLATLAND_PER_STEP_TIMEOUT\"] = \"2\"\n",
+ "osEnv2[\"FLATLAND_MAX_SUCCESSIVE_TIMEOUTS\"] = \"2\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Create the python command for `service.py`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "python -m flatland.evaluators.service --test_folder /home3/jeremy/projects/aicrowd/rl-trains/flatland5/env_data/tests/service_test/ --pickle\n",
+ "['python', '-m', 'flatland.evaluators.service', '--test_folder', '/home3/jeremy/projects/aicrowd/rl-trains/flatland5/env_data/tests/service_test/', '--pickle']\n"
+ ]
+ }
+ ],
+ "source": [
+ "#sCmd = \"python -m flatland.evaluators.service --test_folder ../env_data/tests/service_test --mergeDir ./tmp/merge --actionDir ./tmp/actions --pickle --missingOnly\"\n",
+ "#sCmd = \"python -m flatland.evaluators.service --test_folder ../env_data/tests/service_test --pickle\" # --verbose\"\n",
+ "sCmd = f\"python -m flatland.evaluators.service --test_folder {sDirRoot} --pickle\" # --verbose\"\n",
+ "lsCmd = shlex.split(sCmd)\n",
+ "print(sCmd)\n",
+ "print(lsCmd)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Run the command with Popen (output goes to jupyter stdout not notebook)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "oPipe = sp.Popen(lsCmd, env=osEnv2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "oPipe.poll()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#oFRC = FlatlandRemoteClient(test_envs_root=\"../env_data/tests/service_test/\", verbose=False, use_pickle=True)\n",
+ "oFRC = FlatlandRemoteClient(test_envs_root=sDirRoot, verbose=False, use_pickle=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#env, env_dict = RailEnvPersister.load_new(\"../env_data/tests/service_test/Test_0/Level_0.pkl\") # env_file)\n",
+ "env, env_dict = RailEnvPersister.load_new(f\"{sDirRoot}/Test_0/Level_0.pkl\") # env_file)\n",
+ "ldActions = env_dict[\"actions\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def expert_controller(obs, _env):\n",
+ " return ldActions[_env._elapsed_steps]\n",
+ "\n",
+ "def random_controller(obs, _env):\n",
+ " dAct = {}\n",
+ " for iAg in range(len(_env.agents)):\n",
+ " dAct[iAg] = np.random.randint(0, 5)\n",
+ " return dAct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "oObsB = DummyObservationBuilder()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "oObsB.get()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def run_submission(slow_ep=1, delay=2):\n",
+ " episode = 0\n",
+ " obs = True\n",
+ " while obs:\n",
+ " obs, info = oFRC.env_create(obs_builder_object=oObsB)\n",
+ " if not obs:\n",
+ " print(\"null observation - all envs completed!\")\n",
+ " break\n",
+ " print(f\"Episode : {episode}\")\n",
+ " \n",
+ "\n",
+ " print(oFRC.env.dones['__all__'])\n",
+ "\n",
+ " while True:\n",
+ " if episode < 3:\n",
+ " action = expert_controller(obs, oFRC.env)\n",
+ " else:\n",
+ " action = random_controller(obs, oFRC.env)\n",
+ " \n",
+ " time_start = time.time()\n",
+ " \n",
+ " if (episode == slow_ep) and (oFRC.env._elapsed_steps > 10):\n",
+ " time.sleep(2)\n",
+ " \n",
+ " try:\n",
+ " observation, all_rewards, done, info = oFRC.env_step(action)\n",
+ " time_diff = time.time() - time_start\n",
+ " print(\".\", end=\"\")\n",
+ " if done['__all__']:\n",
+ " print(\"\\nCompleted Episode : \", episode)\n",
+ " print(\"Reward : \", sum(list(all_rewards.values())))\n",
+ " break\n",
+ " except TimeoutException as err:\n",
+ " print(\"Timeout: \", err)\n",
+ " break\n",
+ " \n",
+ " episode += 1\n",
+ " \n",
+ " print(f\"Evaluation Complete - episodes={episode} - send submit message...\")\n",
+ " print(oFRC.submit())\n",
+ " print(\"All done.\")\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "DEPRECATED - use FileMalfunctionGen instead of malfunction_from_file\n",
+ "DEPRECATED - RailEnv arg: malfunction_and_process_data - use malfunction_generator\n",
+ "Episode : 0\n",
+ "False\n",
+ "...........................................................................................................................................................................\n",
+ "Completed Episode : 0\n",
+ "Reward : 10.0\n",
+ "DEPRECATED - use FileMalfunctionGen instead of malfunction_from_file\n",
+ "DEPRECATED - RailEnv arg: malfunction_and_process_data - use malfunction_generator\n",
+ "Episode : 1\n",
+ "False\n",
+ "...........Error received: {'type': 'FLATLAND_RL.ENV_STEP_TIMEOUT'}\n",
+ "Timeout: FLATLAND_RL.ENV_STEP_TIMEOUT\n",
+ "null observation - all envs completed!\n",
+ "Evaluation Complete - episodes=2 - send submit message...\n",
+ "====================================================================================================\n",
+ "====================================================================================================\n",
+ "## Client Performance Stats\n",
+ "====================================================================================================\n",
+ "\t - env_creation_wait_time\t => min: 0.0015025138854980469 || mean: 0.004898786544799805 || max: 0.008767366409301758\n",
+ "\t - internal_env_reset_time\t => min: 0.0014307498931884766 || mean: 0.0017570257186889648 || max: 0.002083301544189453\n",
+ "\t - inference_time(approx)\t => min: 2.2172927856445312e-05 || mean: 0.010976547751921773 || max: 2.0020840167999268\n",
+ "\t - internal_env_step_time\t => min: 0.0003039836883544922 || mean: 0.0007923906976050074 || max: 0.0018360614776611328\n",
+ "====================================================================================================\n",
+ "{'mean_reward': 1978.0, 'mean_normalized_reward': 0.40367, 'mean_percentage_complete': 0.5}\n",
+ "All done.\n"
+ ]
+ }
+ ],
+ "source": [
+ "try:\n",
+ " run_submission()\n",
+ "except TimeoutException as timeoutException:\n",
+ " print(\"Timed out.\")\n",
+ " print(timeoutException)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Kill the evaluator process we started earlier"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!ps -ef | grep -i python | grep -i flatland.evaluators.service | awk '{print $2}' | xargs kill"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
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+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
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+ " <th>1</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>filename</th>\n",
+ " <td>Test_0/Level_0.pkl</td>\n",
+ " <td>Test_0/Level_1.pkl</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>test_id</th>\n",
+ " <td>Test_0</td>\n",
+ " <td>Test_0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>env_id</th>\n",
+ " <td>Level_0</td>\n",
+ " <td>Level_1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>n_agents</th>\n",
+ " <td>5</td>\n",
+ " <td>5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>x_dim</th>\n",
+ " <td>25</td>\n",
+ " <td>25</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>y_dim</th>\n",
+ " <td>25</td>\n",
+ " <td>25</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>n_cities</th>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max_rails_in_city</th>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>malfunction_interval</th>\n",
+ " <td>50</td>\n",
+ " <td>50</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>n_envs_run</th>\n",
+ " <td>50</td>\n",
+ " <td>50</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>seed</th>\n",
+ " <td>11111</td>\n",
+ " <td>11111</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>grid_mode</th>\n",
+ " <td>False</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max_rails_between_cities</th>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>malfunction_duration_min</th>\n",
+ " <td>20</td>\n",
+ " <td>20</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>malfunction_duration_max</th>\n",
+ " <td>50</td>\n",
+ " <td>50</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>speed_ratios</th>\n",
+ " <td>{1.0: 1.0}</td>\n",
+ " <td>{1.0: 1.0}</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>reward</th>\n",
+ " <td>-944</td>\n",
+ " <td>4900</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>normalized_reward</th>\n",
+ " <td>0.807347</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>percentage_complete</th>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>steps</th>\n",
+ " <td>171</td>\n",
+ " <td>12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>simulation_time</th>\n",
+ " <td>0.189325</td>\n",
+ " <td>2.03193</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>nb_malfunctioning_trains</th>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>nb_deadlocked_trains</th>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>controller_inference_time_min</th>\n",
+ " <td>2.21729e-05</td>\n",
+ " <td>2.21729e-05</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>controller_inference_time_mean</th>\n",
+ " <td>3.48147e-05</td>\n",
+ " <td>3.63968e-05</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>controller_inference_time_max</th>\n",
+ " <td>0.000115633</td>\n",
+ " <td>0.000169754</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " 0 1\n",
+ "filename Test_0/Level_0.pkl Test_0/Level_1.pkl\n",
+ "test_id Test_0 Test_0\n",
+ "env_id Level_0 Level_1\n",
+ "n_agents 5 5\n",
+ "x_dim 25 25\n",
+ "y_dim 25 25\n",
+ "n_cities 2 2\n",
+ "max_rails_in_city 3 3\n",
+ "malfunction_interval 50 50\n",
+ "n_envs_run 50 50\n",
+ "seed 11111 11111\n",
+ "grid_mode False False\n",
+ "max_rails_between_cities 2 2\n",
+ "malfunction_duration_min 20 20\n",
+ "malfunction_duration_max 50 50\n",
+ "speed_ratios {1.0: 1.0} {1.0: 1.0}\n",
+ "reward -944 4900\n",
+ "normalized_reward 0.807347 0\n",
+ "percentage_complete 1 0\n",
+ "steps 171 12\n",
+ "simulation_time 0.189325 2.03193\n",
+ "nb_malfunctioning_trains 2 0\n",
+ "nb_deadlocked_trains 0 0\n",
+ "controller_inference_time_min 2.21729e-05 2.21729e-05\n",
+ "controller_inference_time_mean 3.48147e-05 3.63968e-05\n",
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+ ]
+ },
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+ ]
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diff --git a/setup.cfg b/setup.cfg
index 4e4fd8d90b0e848ea5f7c91965445f6772b97b49..cf0c6cc0825f60a55a3e7cce69295103fe5f40cb 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -1,5 +1,5 @@
[bumpversion]
-current_version = 2.2.1
+current_version = 2.2.2
commit = True
tag = True
@@ -22,4 +22,3 @@ test = pytest
[tool:pytest]
collect_ignore = ['setup.py']
-
diff --git a/setup.py b/setup.py
index 23fec831eca74a7a3129ce37de551c73885dface..cb09748e955972df03586fcf9ede33e7647cd79d 100644
--- a/setup.py
+++ b/setup.py
@@ -80,6 +80,6 @@ setup(
test_suite='tests',
tests_require=test_requirements,
url='https://gitlab.aicrowd.com/flatland/flatland',
- version='2.2.1',
+ version='2.2.2',
zip_safe=False,
)
diff --git a/tox.ini b/tox.ini
index 0023ee34bca2e26f30199bc2e9b2c93bdf779dd2..4054955ea6cf0463849db959847b1f321f4983b7 100644
--- a/tox.ini
+++ b/tox.ini
@@ -128,14 +128,17 @@ commands =
; TODO should examples be run with py36 and py37??
basepython = python3.6
setenv =
- PYTHONPATH = {toxinidir}
+ PYTHONPATH = {envdir}
+ ;{toxinidir}
passenv =
DISPLAY
XAUTHORITY
; HTTP_PROXY+HTTPS_PROXY required behind corporate proxies
HTTP_PROXY
HTTPS_PROXY
-whitelist_externals = sh
+whitelist_externals = sh
+ bash
+ pwd
deps =
-r{toxinidir}/requirements_dev.txt
-r{toxinidir}/requirements_continuous_integration.txt
@@ -147,6 +150,8 @@ conda_channels :
; run tests from subfolder to ensure that resources are accessed via resources and not via relative paths
changedir = {envtmpdir}/6f59bc68108c3895b1828abdd04b9a06
commands =
+ bash -c "pwd"
+ bash -c "echo $PYTHONPATH"
python -m jupyter nbextension install --py --sys-prefix widgetsnbextension
python -m jupyter nbextension enable --py --sys-prefix widgetsnbextension
python -m jupyter nbextension install --py --sys-prefix jpy_canvas