diff --git a/examples/flatland_2_0_example.py b/examples/flatland_2_0_example.py
index 71a185c765bcab831e7b104124a164bcf2398b14..a99009a5d5e4f564c15efe981d76b2c0c6cebdfa 100644
--- a/examples/flatland_2_0_example.py
+++ b/examples/flatland_2_0_example.py
@@ -1,9 +1,9 @@
import numpy as np
-from flatland.envs.rail_generators import sparse_rail_generator
from flatland.envs.observations import TreeObsForRailEnv
from flatland.envs.predictions import ShortestPathPredictorForRailEnv
from flatland.envs.rail_env import RailEnv
+from flatland.envs.rail_generators import sparse_rail_generator
from flatland.envs.schedule_generators import sparse_schedule_generator
from flatland.utils.rendertools import RenderTool
@@ -13,27 +13,35 @@ np.random.seed(1)
# Training on simple small tasks is the best way to get familiar with the environment
# Use a the malfunction generator to break agents from time to time
-stochastic_data = {'prop_malfunction': 0.5, # Percentage of defective agents
+stochastic_data = {'prop_malfunction': 0.1, # Percentage of defective agents
'malfunction_rate': 30, # Rate of malfunction occurence
'min_duration': 3, # Minimal duration of malfunction
- 'max_duration': 10 # Max duration of malfunction
+ 'max_duration': 20 # Max duration of malfunction
}
+# Custom observation builder
TreeObservation = TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv())
-env = RailEnv(width=20,
- height=20,
- rail_generator=sparse_rail_generator(num_cities=2, # Number of cities in map (where train stations are)
- num_intersections=1, # Number of intersections (no start / target)
+
+# Different agent types (trains) with different speeds.
+speed_ration_map = {1.: 0.25, # Fast passenger train
+ 1. / 2.: 0.25, # Fast freight train
+ 1. / 3.: 0.25, # Slow commuter train
+ 1. / 4.: 0.25} # Slow freight train
+
+env = RailEnv(width=50,
+ height=50,
+ rail_generator=sparse_rail_generator(num_cities=20, # Number of cities in map (where train stations are)
+ num_intersections=5, # Number of intersections (no start / target)
num_trainstations=15, # Number of possible start/targets on map
min_node_dist=3, # Minimal distance of nodes
- node_radius=3, # Proximity of stations to city center
- num_neighb=2, # Number of connections to other cities/intersections
+ node_radius=2, # Proximity of stations to city center
+ num_neighb=4, # Number of connections to other cities/intersections
seed=15, # Random seed
realistic_mode=True,
enhance_intersection=True
),
- schedule_generator=sparse_schedule_generator(),
- number_of_agents=5,
+ schedule_generator=sparse_schedule_generator(speed_ration_map),
+ number_of_agents=10,
stochastic_data=stochastic_data, # Malfunction data generator
obs_builder_object=TreeObservation)
@@ -83,10 +91,6 @@ action_dict = dict()
print("Start episode...")
# Reset environment and get initial observations for all agents
obs = env.reset()
-# Update/Set agent's speed
-for idx in range(env.get_num_agents()):
- speed = 1.0 / ((idx % 5) + 1.0)
- env.agents[idx].speed_data["speed"] = speed
# Reset the rendering sytem
env_renderer.reset()
diff --git a/flatland/envs/agent_utils.py b/flatland/envs/agent_utils.py
index 4c4070088c59499f885c16db68976c163ec91001..b228e10b6c146f5692166e179bb9f574a68c9134 100644
--- a/flatland/envs/agent_utils.py
+++ b/flatland/envs/agent_utils.py
@@ -30,10 +30,11 @@ class EnvAgentStatic(object):
lambda: dict({'malfunction': 0, 'malfunction_rate': 0, 'next_malfunction': 0, 'nr_malfunctions': 0})))
@classmethod
- def from_lists(cls, positions, directions, targets, speeds=None):
+ def from_lists(cls, positions, directions, targets, speeds=None, malfunction_rates=None):
""" Create a list of EnvAgentStatics from lists of positions, directions and targets
"""
speed_datas = []
+
for i in range(len(positions)):
speed_datas.append({'position_fraction': 0.0,
'speed': speeds[i] if speeds is not None else 1.0,
@@ -41,10 +42,11 @@ class EnvAgentStatic(object):
# TODO: on initialization, all agents are re-set as non-broken. Perhaps it may be desirable to set
# some as broken?
+
malfunction_datas = []
for i in range(len(positions)):
malfunction_datas.append({'malfunction': 0,
- 'malfunction_rate': 0,
+ 'malfunction_rate': malfunction_rates[i] if malfunction_rates is not None else 0.,
'next_malfunction': 0,
'nr_malfunctions': 0})
diff --git a/flatland/envs/rail_env.py b/flatland/envs/rail_env.py
index 62efbdc5bc0781c4c7482412dafd98710ed9d14e..280fd345d8c1db206c42dc30ba2d7b5fa2e8a69e 100644
--- a/flatland/envs/rail_env.py
+++ b/flatland/envs/rail_env.py
@@ -238,7 +238,6 @@ class RailEnv(Environment):
agents_hints = optionals['agents_hints']
self.agents_static = EnvAgentStatic.from_lists(
*self.schedule_generator(self.rail, self.get_num_agents(), hints=agents_hints))
-
self.restart_agents()
for i_agent in range(self.get_num_agents()):
@@ -248,7 +247,6 @@ class RailEnv(Environment):
if np.random.random() < self.proportion_malfunctioning_trains:
agent.malfunction_data['malfunction_rate'] = self.mean_malfunction_rate
- agent.speed_data['position_fraction'] = 0.0
agent.malfunction_data['malfunction'] = 0
self._agent_malfunction(agent)
@@ -510,11 +508,9 @@ class RailEnv(Environment):
grid_data = self.rail.grid.tolist()
agent_static_data = [agent.to_list() for agent in self.agents_static]
agent_data = [agent.to_list() for agent in self.agents]
-
- msgpack.packb(grid_data)
- msgpack.packb(agent_data)
- msgpack.packb(agent_static_data)
-
+ msgpack.packb(grid_data, use_bin_type=True)
+ msgpack.packb(agent_data, use_bin_type=True)
+ msgpack.packb(agent_static_data, use_bin_type=True)
msg_data = {
"grid": grid_data,
"agents_static": agent_static_data,
@@ -528,11 +524,11 @@ class RailEnv(Environment):
return msgpack.packb(msg_data, use_bin_type=True)
def set_full_state_msg(self, msg_data):
- data = msgpack.unpackb(msg_data, use_list=False)
- self.rail.grid = np.array(data[b"grid"])
+ data = msgpack.unpackb(msg_data, use_list=False, encoding='utf-8')
+ self.rail.grid = np.array(data["grid"])
# agents are always reset as not moving
- self.agents_static = [EnvAgentStatic(d[0], d[1], d[2], moving=False) for d in data[b"agents_static"]]
- self.agents = [EnvAgent(d[0], d[1], d[2], d[3], d[4]) for d in data[b"agents"]]
+ self.agents_static = [EnvAgentStatic(d[0], d[1], d[2], moving=False) for d in data["agents_static"]]
+ self.agents = [EnvAgent(d[0], d[1], d[2], d[3], d[4], d[5], d[6], d[7], d[8]) for d in data["agents"]]
# setup with loaded data
self.height, self.width = self.rail.grid.shape
self.rail.height = self.height
@@ -540,13 +536,13 @@ class RailEnv(Environment):
self.dones = dict.fromkeys(list(range(self.get_num_agents())) + ["__all__"], False)
def set_full_state_dist_msg(self, msg_data):
- data = msgpack.unpackb(msg_data, use_list=False)
- self.rail.grid = np.array(data[b"grid"])
+ data = msgpack.unpackb(msg_data, use_list=False, encoding='utf-8')
+ self.rail.grid = np.array(data["grid"])
# agents are always reset as not moving
- self.agents_static = [EnvAgentStatic(d[0], d[1], d[2], moving=False) for d in data[b"agents_static"]]
- self.agents = [EnvAgent(d[0], d[1], d[2], d[3], d[4]) for d in data[b"agents"]]
- if hasattr(self.obs_builder, 'distance_map') and b"distance_maps" in data.keys():
- self.obs_builder.distance_map = data[b"distance_maps"]
+ self.agents_static = [EnvAgentStatic(d[0], d[1], d[2], moving=False) for d in data["agents_static"]]
+ self.agents = [EnvAgent(d[0], d[1], d[2], d[3], d[4], d[5], d[6], d[7], d[8]) for d in data["agents"]]
+ if hasattr(self.obs_builder, 'distance_map') and "distance_maps" in data.keys():
+ self.obs_builder.distance_map = data["distance_maps"]
# setup with loaded data
self.height, self.width = self.rail.grid.shape
self.rail.height = self.height
@@ -557,13 +553,12 @@ class RailEnv(Environment):
grid_data = self.rail.grid.tolist()
agent_static_data = [agent.to_list() for agent in self.agents_static]
agent_data = [agent.to_list() for agent in self.agents]
-
- msgpack.packb(grid_data)
- msgpack.packb(agent_data)
- msgpack.packb(agent_static_data)
+ msgpack.packb(grid_data, use_bin_type=True)
+ msgpack.packb(agent_data, use_bin_type=True)
+ msgpack.packb(agent_static_data, use_bin_type=True)
if hasattr(self.obs_builder, 'distance_map'):
distance_map_data = self.obs_builder.distance_map
- msgpack.packb(distance_map_data)
+ msgpack.packb(distance_map_data, use_bin_type=True)
msg_data = {
"grid": grid_data,
"agents_static": agent_static_data,
diff --git a/flatland/envs/rail_generators.py b/flatland/envs/rail_generators.py
index 40ec2e0df89a3de48bf0a2a4430de3e30fa556e5..b69d80a065670ebafb60da2972d31efb05add047 100644
--- a/flatland/envs/rail_generators.py
+++ b/flatland/envs/rail_generators.py
@@ -573,9 +573,14 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
x_positions = np.linspace(node_radius, height - node_radius, nodes_per_row, dtype=int)
y_positions = np.linspace(node_radius, width - node_radius, nodes_per_col, dtype=int)
+ fraction = 0
+ city_fraction = num_cities / tot_num_node
+ step = np.gcd(num_intersections, num_cities) / tot_num_node
+
for node_idx in range(num_cities + num_intersections):
to_close = True
tries = 0
+
if not realistic_mode:
while to_close:
x_tmp = node_radius + np.random.randint(height - node_radius)
@@ -587,7 +592,7 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
if distance_on_rail((x_tmp, y_tmp), node_pos) < min_node_dist:
to_close = True
- # CHeck distance to intersections
+ # Check distance to intersections
for node_pos in intersection_positions:
if distance_on_rail((x_tmp, y_tmp), node_pos) < min_node_dist:
to_close = True
@@ -603,13 +608,13 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
warnings.warn("Could not set nodes, please change initial parameters!!!!")
break
else:
+ fraction = (fraction + step) % 1.
x_tmp = x_positions[node_idx % nodes_per_row]
y_tmp = y_positions[node_idx // nodes_per_row]
- if len(city_positions) < num_cities and (node_idx % (tot_num_node // num_cities)) == 0:
+ if len(city_positions) < num_cities and fraction < city_fraction:
city_positions.append((x_tmp, y_tmp))
else:
intersection_positions.append((x_tmp, y_tmp))
-
node_positions = city_positions + intersection_positions
# Chose node connection
@@ -627,14 +632,15 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
current_node = node_stack[0]
delete_idx = np.where(available_nodes_full == current_node)
available_nodes_full = np.delete(available_nodes_full, delete_idx, 0)
+
# Priority city to intersection connections
- if current_node < num_cities and len(available_intersections) > 0:
+ if False and current_node < num_cities and len(available_intersections) > 0:
available_nodes = available_intersections
delete_idx = np.where(available_cities == current_node)
available_cities = np.delete(available_cities, delete_idx, 0)
# Priority intersection to city connections
- elif current_node >= num_cities and len(available_cities) > 0:
+ elif False and current_node >= num_cities and len(available_cities) > 0:
available_nodes = available_cities
delete_idx = np.where(available_intersections == current_node)
available_intersections = np.delete(available_intersections, delete_idx, 0)
@@ -672,8 +678,8 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
if num_cities > 1:
train_stations = [[] for i in range(num_cities)]
built_num_trainstation = 0
- spot_found = True
for station in range(num_trainstations):
+ spot_found = True
trainstation_node = int(station / num_trainstations * num_cities)
station_x = np.clip(node_positions[trainstation_node][0] + np.random.randint(-node_radius, node_radius),
@@ -699,7 +705,7 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
if tries > 100:
warnings.warn("Could not set trainstations, please change initial parameters!!!!")
spot_found = False
- break
+
if spot_found:
train_stations[trainstation_node].append((station_x, station_y))
@@ -708,12 +714,12 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
(station_x, station_y))
# Check if connection was made
if len(connection) == 0:
- train_stations[trainstation_node].pop(-1)
+ if len(train_stations[trainstation_node]) > 0:
+ train_stations[trainstation_node].pop(-1)
else:
built_num_trainstation += 1
# Adjust the number of agents if you could not build enough trainstations
-
if num_agents > built_num_trainstation:
num_agents = built_num_trainstation
warnings.warn("sparse_rail_generator: num_agents > nr_start_goal, changing num_agents")
diff --git a/flatland/envs/schedule_generators.py b/flatland/envs/schedule_generators.py
index 4843e0040d80b79de54e8ed57674a37884ef6809..a3a6dc1e4813a8c41284d46b6c03c5898cdcc63e 100644
--- a/flatland/envs/schedule_generators.py
+++ b/flatland/envs/schedule_generators.py
@@ -60,7 +60,10 @@ def sparse_schedule_generator(speed_ratio_map: Mapping[float, float] = None) ->
def generator(rail: GridTransitionMap, num_agents: int, hints: Any = None):
train_stations = hints['train_stations']
agent_start_targets_nodes = hints['agent_start_targets_nodes']
- num_agents = hints['num_agents']
+ max_num_agents = hints['num_agents']
+ if num_agents > max_num_agents:
+ num_agents = max_num_agents
+ warnings.warn("Too many agents! Changes number of agents.")
# Place agents and targets within available train stations
agents_position = []
agents_target = []
@@ -191,7 +194,8 @@ def random_schedule_generator(speed_ratio_map: Mapping[float, float] = None) ->
if len(valid_starting_directions) == 0:
update_agents[i] = 1
- warnings.warn("reset position for agent[{}]: {} -> {}".format(i, agents_position[i], agents_target[i]))
+ warnings.warn(
+ "reset position for agent[{}]: {} -> {}".format(i, agents_position[i], agents_target[i]))
re_generate = True
break
else:
@@ -221,15 +225,25 @@ def schedule_from_file(filename) -> ScheduleGenerator:
def generator(rail: GridTransitionMap, num_agents: int, hints: Any = None) -> ScheduleGeneratorProduct:
with open(filename, "rb") as file_in:
load_data = file_in.read()
- data = msgpack.unpackb(load_data, use_list=False)
+ data = msgpack.unpackb(load_data, use_list=False, encoding='utf-8')
# agents are always reset as not moving
- agents_static = [EnvAgentStatic(d[0], d[1], d[2], moving=False) for d in data[b"agents_static"]]
+ if len(data['agents_static'][0]) > 5:
+ print(len(data['agents_static'][0]))
+ agents_static = [EnvAgentStatic(d[0], d[1], d[2], d[3], d[4], d[5]) for d in data["agents_static"]]
+ else:
+ agents_static = [EnvAgentStatic(d[0], d[1], d[2], d[3]) for d in data["agents_static"]]
+
# setup with loaded data
agents_position = [a.position for a in agents_static]
agents_direction = [a.direction for a in agents_static]
agents_target = [a.target for a in agents_static]
-
- return agents_position, agents_direction, agents_target, [1.0] * len(agents_position)
+ if len(data['agents_static'][0]) > 5:
+ agents_speed = [a.speed_data['speed'] for a in agents_static]
+ agents_malfunction = [a.malfunction_data['malfunction_rate'] for a in agents_static]
+ else:
+ agents_speed = None
+ agents_malfunction = None
+ return agents_position, agents_direction, agents_target, agents_speed, agents_malfunction
return generator
diff --git a/flatland/evaluators/client.py b/flatland/evaluators/client.py
index f2dac1c8705a651e2a7be026b4bd82a961efbbdd..92f1a49d777552b2108aff3963aa5c1bc84fdbfc 100644
--- a/flatland/evaluators/client.py
+++ b/flatland/evaluators/client.py
@@ -254,14 +254,12 @@ class FlatlandRemoteClient(object):
if __name__ == "__main__":
remote_client = FlatlandRemoteClient()
-
def my_controller(obs, _env):
_action = {}
for _idx, _ in enumerate(_env.agents):
_action[_idx] = np.random.randint(0, 5)
return _action
-
my_observation_builder = TreeObsForRailEnv(max_depth=3,
predictor=ShortestPathPredictorForRailEnv())
diff --git a/flatland/utils/simple_rail.py b/flatland/utils/simple_rail.py
index 67bd93dd35c8f53ef3cdef23dbae0f0d785b9a64..3dfa6bad0d05d8519b233422593cd7f9c2c460f3 100644
--- a/flatland/utils/simple_rail.py
+++ b/flatland/utils/simple_rail.py
@@ -81,6 +81,7 @@ def make_simple_rail2() -> Tuple[GridTransitionMap, np.array]:
rail.grid = rail_map
return rail, rail_map
+
def make_simple_rail_unconnected() -> Tuple[GridTransitionMap, np.array]:
# We instantiate a very simple rail network on a 7x10 grid:
# Note that that cells have invalid RailEnvTransitions!
@@ -103,13 +104,13 @@ def make_simple_rail_unconnected() -> Tuple[GridTransitionMap, np.array]:
horizontal_straight = transitions.rotate_transition(vertical_straight, 90)
simple_switch_north_left = cells[2]
simple_switch_north_right = cells[10]
- simple_switch_east_west_north = transitions.rotate_transition(simple_switch_north_right, 270)
+ # simple_switch_east_west_north = transitions.rotate_transition(simple_switch_north_right, 270)
simple_switch_east_west_south = transitions.rotate_transition(simple_switch_north_left, 270)
rail_map = np.array(
[[empty] * 3 + [dead_end_from_south] + [empty] * 6] +
- [[empty] * 3 + [vertical_straight] + [empty] * 6] +
- [[empty] * 3 + [dead_end_from_north] + [empty] * 6] +
- [[dead_end_from_east] + [horizontal_straight] * 5 + [simple_switch_east_west_south] +
+ [[empty] * 3 + [vertical_straight] + [empty] * 6] +
+ [[empty] * 3 + [dead_end_from_north] + [empty] * 6] +
+ [[dead_end_from_east] + [horizontal_straight] * 5 + [simple_switch_east_west_south] +
[horizontal_straight] * 2 + [dead_end_from_west]] +
[[empty] * 6 + [vertical_straight] + [empty] * 3] * 2 +
[[empty] * 6 + [dead_end_from_north] + [empty] * 3], dtype=np.uint16)
diff --git a/tests/test_flatland_core_transition_map.py b/tests/test_flatland_core_transition_map.py
index 8812c847e61d81f6614f37d26489b4c17ea7fd14..1137b8816973c12601029543c221810c9acd157c 100644
--- a/tests/test_flatland_core_transition_map.py
+++ b/tests/test_flatland_core_transition_map.py
@@ -116,18 +116,18 @@ def test_path_exists(rendering=False):
check_path(
env,
rail,
- (1,3), # east dead-end
+ (1, 3), # east dead-end
2, # south
- (3,3), # north dead-end
+ (3, 3), # north dead-end
True
)
check_path(
env,
rail,
- (1,3), # east dead-end
+ (1, 3), # east dead-end
0, # north
- (3,3), # north dead-end
+ (3, 3), # north dead-end
True
)