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tests/test_pettingzoo_interface.py 0 → 100644
View file @ a6c4ae6a
import pytest
@pytest.mark.skip(reason="Only for testing pettingzoo interface and wrappers")
def test_petting_zoo_interface_env():
import numpy as np
import os
import PIL
import shutil
from flatland.contrib.interface import flatland_env
from flatland.contrib.utils import env_generators
from flatland.envs.observations import TreeObsForRailEnv
from flatland.envs.predictions import ShortestPathPredictorForRailEnv
# First of all we import the Flatland rail environment
from flatland.utils.rendertools import RenderTool, AgentRenderVariant
from flatland.contrib.wrappers.flatland_wrappers import SkipNoChoiceCellsWrapper
from flatland.contrib.wrappers.flatland_wrappers import ShortestPathActionWrapper # noqa
# Custom observation builder without predictor
# observation_builder = GlobalObsForRailEnv()
# Custom observation builder with predictor
observation_builder = TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv(30))
seed = 11
save = True
np.random.seed(seed)
experiment_name = "flatland_pettingzoo"
total_episodes = 2
if save:
try:
if os.path.isdir(experiment_name):
shutil.rmtree(experiment_name)
os.mkdir(experiment_name)
except OSError as e:
print("Error: %s - %s." % (e.filename, e.strerror))
rail_env = env_generators.sparse_env_small(seed, observation_builder)
rail_env = env_generators.small_v0(seed, observation_builder)
rail_env.reset(random_seed=seed)
# For Shortest Path Action Wrapper, change action to 1
# rail_env = ShortestPathActionWrapper(rail_env)
rail_env = SkipNoChoiceCellsWrapper(rail_env, accumulate_skipped_rewards=False, discounting=0.0)
dones = {}
dones['__all__'] = False
step = 0
ep_no = 0
frame_list = []
all_actions_env = []
all_actions_pettingzoo_env = []
# while not dones['__all__']:
while ep_no < total_episodes:
action_dict = {}
# Chose an action for each agent
for a in range(rail_env.get_num_agents()):
# action = env_generators.get_shortest_path_action(rail_env, a)
action = 2
all_actions_env.append(action)
action_dict.update({a: action})
step += 1
# Do the environment step
observations, rewards, dones, information = rail_env.step(action_dict)
frame_list.append(PIL.Image.fromarray(rail_env.render(mode="rgb_array")))
if dones['__all__']:
completion = env_generators.perc_completion(rail_env)
print("Final Agents Completed:", completion)
ep_no += 1
if save:
frame_list[0].save(f"{experiment_name}{os.sep}out_{ep_no}.gif", save_all=True,
append_images=frame_list[1:], duration=3, loop=0)
frame_list = []
rail_env.reset(random_seed=seed+ep_no)
# __sphinx_doc_begin__
env = flatland_env.env(environment=rail_env)
seed = 11
env.reset(random_seed=seed)
step = 0
ep_no = 0
frame_list = []
while ep_no < total_episodes:
for agent in env.agent_iter():
obs, reward, done, info = env.last()
# act = env_generators.get_shortest_path_action(env.environment, get_agent_handle(agent))
act = 2
all_actions_pettingzoo_env.append(act)
env.step(act)
frame_list.append(PIL.Image.fromarray(env.render(mode='rgb_array')))
step += 1
# __sphinx_doc_end__
completion = env_generators.perc_completion(env)
print("Final Agents Completed:", completion)
ep_no += 1
if save:
frame_list[0].save(f"{experiment_name}{os.sep}pettyzoo_out_{ep_no}.gif", save_all=True,
append_images=frame_list[1:], duration=3, loop=0)
frame_list = []
env.close()
env.reset(random_seed=seed+ep_no)
min_len = min(len(all_actions_pettingzoo_env), len(all_actions_env))
assert all_actions_pettingzoo_env[:min_len] == all_actions_env[:min_len], "actions do not match"
if __name__ == "__main__":
import pytest
import sys
sys.exit(pytest.main(["-sv", __file__]))
tests/test_random_seeding.py
View file @ a6c4ae6a
...@@ -3,24 +3,20 @@ import numpy as np ...@@ -3,24 +3,20 @@ import numpy as np
from flatland.envs.observations import GlobalObsForRailEnv, TreeObsForRailEnv from flatland.envs.observations import GlobalObsForRailEnv, TreeObsForRailEnv
from flatland.envs.predictions import ShortestPathPredictorForRailEnv from flatland.envs.predictions import ShortestPathPredictorForRailEnv
from flatland.envs.rail_env import RailEnv from flatland.envs.rail_env import RailEnv
from flatland.envs.rail_generators import rail_from_grid_transition_map from flatland.envs.rail_generators import rail_from_grid_transition_map, sparse_rail_generator
from flatland.envs.schedule_generators import random_schedule_generator from flatland.envs.line_generators import sparse_line_generator
from flatland.utils.simple_rail import make_simple_rail2 from flatland.utils.simple_rail import make_simple_rail2
def test_random_seeding(): def ndom_seeding():
# Set fixed malfunction duration for this test # Set fixed malfunction duration for this test
rail, rail_map = make_simple_rail2() rail, rail_map, optionals = make_simple_rail2()
# Move target to unreachable position in order to not interfere with test # Move target to unreachable position in order to not interfere with test
for idx in range(100): for idx in range(100):
env = RailEnv(width=25, env = RailEnv(width=25, height=30, rail_generator=rail_from_grid_transition_map(rail, optionals),
height=30, line_generator=sparse_line_generator(seed=12), number_of_agents=10)
rail_generator=rail_from_grid_transition_map(rail), env.reset(True, True, random_seed=1)
schedule_generator=random_schedule_generator(seed=12),
number_of_agents=10
)
env.reset(True, True, False, random_seed=1)
env.agents[0].target = (0, 0) env.agents[0].target = (0, 0)
for step in range(10): for step in range(10):
...@@ -48,29 +44,20 @@ def test_random_seeding(): ...@@ -48,29 +44,20 @@ def test_random_seeding():
def test_seeding_and_observations(): def test_seeding_and_observations():
# Test if two different instances diverge with different observations # Test if two different instances diverge with different observations
rail, rail_map = make_simple_rail2() rail, rail_map, optionals = make_simple_rail2()
optionals['agents_hints']['num_agents'] = 10
# Make two seperate envs with different observation builders # Make two seperate envs with different observation builders
# Global Observation # Global Observation
env = RailEnv(width=25, env = RailEnv(width=25, height=30, rail_generator=rail_from_grid_transition_map(rail, optionals),
height=30, line_generator=sparse_line_generator(seed=12), number_of_agents=10,
rail_generator=rail_from_grid_transition_map(rail), obs_builder_object=GlobalObsForRailEnv())
schedule_generator=random_schedule_generator(seed=12),
number_of_agents=10,
obs_builder_object=GlobalObsForRailEnv()
)
# Tree Observation # Tree Observation
env2 = RailEnv(width=25, env2 = RailEnv(width=25, height=30, rail_generator=rail_from_grid_transition_map(rail, optionals),
height=30, line_generator=sparse_line_generator(seed=12), number_of_agents=10,
rail_generator=rail_from_grid_transition_map(rail), obs_builder_object=TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv()))
schedule_generator=random_schedule_generator(seed=12),
number_of_agents=10,
obs_builder_object=TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv())
)
env.reset(False, False, False, random_seed=12)
env2.reset(False, False, False, random_seed=12)
env.reset(False, False, random_seed=12)
env2.reset(False, False, random_seed=12)
# Check that both environments produce the same initial start positions # Check that both environments produce the same initial start positions
assert env.agents[0].initial_position == env2.agents[0].initial_position assert env.agents[0].initial_position == env2.agents[0].initial_position
assert env.agents[1].initial_position == env2.agents[1].initial_position assert env.agents[1].initial_position == env2.agents[1].initial_position
...@@ -90,9 +77,7 @@ def test_seeding_and_observations(): ...@@ -90,9 +77,7 @@ def test_seeding_and_observations():
action_dict[a] = action action_dict[a] = action
env.step(action_dict) env.step(action_dict)
env2.step(action_dict) env2.step(action_dict)
# Check that both environments end up in the same position # Check that both environments end up in the same position
assert env.agents[0].position == env2.agents[0].position assert env.agents[0].position == env2.agents[0].position
assert env.agents[1].position == env2.agents[1].position assert env.agents[1].position == env2.agents[1].position
assert env.agents[2].position == env2.agents[2].position assert env.agents[2].position == env2.agents[2].position
...@@ -109,8 +94,8 @@ def test_seeding_and_observations(): ...@@ -109,8 +94,8 @@ def test_seeding_and_observations():
def test_seeding_and_malfunction(): def test_seeding_and_malfunction():
# Test if two different instances diverge with different observations # Test if two different instances diverge with different observations
rail, rail_map = make_simple_rail2() rail, rail_map, optionals = make_simple_rail2()
optionals['agents_hints']['num_agents'] = 10
stochastic_data = {'prop_malfunction': 0.4, stochastic_data = {'prop_malfunction': 0.4,
'malfunction_rate': 2, 'malfunction_rate': 2,
'min_duration': 10, 'min_duration': 10,
...@@ -118,27 +103,17 @@ def test_seeding_and_malfunction(): ...@@ -118,27 +103,17 @@ def test_seeding_and_malfunction():
# Make two seperate envs with different and see if the exhibit the same malfunctions # Make two seperate envs with different and see if the exhibit the same malfunctions
# Global Observation # Global Observation
for tests in range(1, 100): for tests in range(1, 100):
env = RailEnv(width=25, env = RailEnv(width=25, height=30, rail_generator=rail_from_grid_transition_map(rail, optionals),
height=30, line_generator=sparse_line_generator(), number_of_agents=10,
rail_generator=rail_from_grid_transition_map(rail), obs_builder_object=GlobalObsForRailEnv())
schedule_generator=random_schedule_generator(),
number_of_agents=10,
obs_builder_object=GlobalObsForRailEnv(),
stochastic_data=stochastic_data, # Malfunction data generator
)
# Tree Observation # Tree Observation
env2 = RailEnv(width=25, env2 = RailEnv(width=25, height=30, rail_generator=rail_from_grid_transition_map(rail, optionals),
height=30, line_generator=sparse_line_generator(), number_of_agents=10,
rail_generator=rail_from_grid_transition_map(rail), obs_builder_object=GlobalObsForRailEnv())
schedule_generator=random_schedule_generator(),
number_of_agents=10, env.reset(True, False, random_seed=tests)
obs_builder_object=GlobalObsForRailEnv(), env2.reset(True, False, random_seed=tests)
stochastic_data=stochastic_data, # Malfunction data generator
)
env.reset(True, False, True, random_seed=tests)
env2.reset(True, False, True, random_seed=tests)
# Check that both environments produce the same initial start positions # Check that both environments produce the same initial start positions
assert env.agents[0].initial_position == env2.agents[0].initial_position assert env.agents[0].initial_position == env2.agents[0].initial_position
...@@ -158,8 +133,8 @@ def test_seeding_and_malfunction(): ...@@ -158,8 +133,8 @@ def test_seeding_and_malfunction():
action = np.random.randint(4) action = np.random.randint(4)
action_dict[a] = action action_dict[a] = action
# print("----------------------") # print("----------------------")
# print(env.agents[a].malfunction_data, env.agents[a].status) # print(env.agents[a].malfunction_handler, env.agents[a].status)
# print(env2.agents[a].malfunction_data, env2.agents[a].status) # print(env2.agents[a].malfunction_handler, env2.agents[a].status)
_, reward1, done1, _ = env.step(action_dict) _, reward1, done1, _ = env.step(action_dict)
_, reward2, done2, _ = env2.step(action_dict) _, reward2, done2, _ = env2.step(action_dict)
...@@ -178,3 +153,66 @@ def test_seeding_and_malfunction(): ...@@ -178,3 +153,66 @@ def test_seeding_and_malfunction():
assert env.agents[7].position == env2.agents[7].position assert env.agents[7].position == env2.agents[7].position
assert env.agents[8].position == env2.agents[8].position assert env.agents[8].position == env2.agents[8].position
assert env.agents[9].position == env2.agents[9].position assert env.agents[9].position == env2.agents[9].position
def test_reproducability_env():
"""
Test that no random generators are present within the env that get influenced by external np random
"""
speed_ration_map = {1.: 1., # Fast passenger train
1. / 2.: 0., # Fast freight train
1. / 3.: 0., # Slow commuter train
1. / 4.: 0.} # Slow freight train
env = RailEnv(width=25, height=30, rail_generator=sparse_rail_generator(max_num_cities=5,
max_rails_between_cities=3,
seed=10, # Random seed
grid_mode=True
),
line_generator=sparse_line_generator(speed_ration_map), number_of_agents=1)
env.reset(True, True, random_seed=1)
excpeted_grid = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 16386, 1025, 4608, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[16386, 17411, 1025, 5633, 17411, 3089, 1025, 1097, 5633, 17411, 1025, 5633, 1025, 1025, 1025, 1025, 5633, 17411, 1025, 1025, 1025, 5633, 17411, 1025, 4608],
[32800, 32800, 0, 72, 3089, 5633, 1025, 17411, 1097, 2064, 0, 72, 1025, 1025, 1025, 1025, 1097, 3089, 1025, 1025, 1025, 1097, 3089, 1025, 37408],
[32800, 32800, 0, 0, 0, 72, 1025, 2064, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32800],
[32800, 32800, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32800],
[32800, 32800, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32800],
[32800, 32872, 4608, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16386, 34864],
[32800, 32800, 32800, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32800, 32800],
[32800, 32800, 32800, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32800, 32800],
[32800, 32800, 32800, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32800, 32800],
[32800, 32800, 32800, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32800, 32800],
[32800, 32800, 32800, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32800, 32800],
[32800, 32800, 32800, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32800, 32800],
[72, 37408, 32800, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32800, 32800],
[0, 49186, 2064, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 72, 37408],
[0, 32800, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32800],
[0, 32800, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32800],
[0, 32800, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32800],
[0, 32800, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 32800],
[0, 32872, 1025, 5633, 17411, 1025, 1025, 1025, 5633, 17411, 1025, 1025, 1025, 1025, 1025, 1025, 5633, 17411, 1025, 1025, 1025, 5633, 17411, 1025, 34864],
[0, 72, 1025, 1097, 3089, 1025, 1025, 1025, 1097, 3089, 1025, 1025, 1025, 1025, 1025, 1025, 1097, 3089, 1025, 1025, 1025, 1097, 3089, 1025, 2064],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
assert env.rail.grid.tolist() == excpeted_grid
# Test that we don't have interference from calling mulitple function outisde
env2 = RailEnv(width=25, height=30, rail_generator=sparse_rail_generator(max_num_cities=5,
max_rails_between_cities=3,
seed=10, # Random seed
grid_mode=True
),
line_generator=sparse_line_generator(speed_ration_map), number_of_agents=1)
np.random.seed(1)
for i in range(10):
np.random.randn()
env2.reset(True, True, random_seed=1)
assert env2.rail.grid.tolist() == excpeted_grid
tests/test_speed_classes.py
View file @ a6c4ae6a
...@@ -2,30 +2,28 @@ ...@@ -2,30 +2,28 @@
import numpy as np import numpy as np
from flatland.envs.rail_env import RailEnv from flatland.envs.rail_env import RailEnv
from flatland.envs.rail_generators import complex_rail_generator from flatland.envs.rail_generators import sparse_rail_generator
from flatland.envs.schedule_generators import speed_initialization_helper, complex_schedule_generator from flatland.envs.line_generators import speed_initialization_helper, sparse_line_generator
def test_speed_initialization_helper(): def test_speed_initialization_helper():
np.random.seed(1) random_generator = np.random.RandomState()
random_generator.seed(10)
speed_ratio_map = {1: 0.3, 2: 0.4, 3: 0.3} speed_ratio_map = {1: 0.3, 2: 0.4, 3: 0.3}
actual_speeds = speed_initialization_helper(10, speed_ratio_map) actual_speeds = speed_initialization_helper(10, speed_ratio_map, np_random=random_generator)
# seed makes speed_initialization_helper deterministic -> check generated speeds. # seed makes speed_initialization_helper deterministic -> check generated speeds.
assert actual_speeds == [2, 3, 1, 2, 1, 1, 1, 2, 2, 2] assert actual_speeds == [3, 1, 2, 3, 2, 1, 1, 3, 1, 1]
def test_rail_env_speed_intializer(): def test_rail_env_speed_intializer():
speed_ratio_map = {1: 0.3, 2: 0.4, 3: 0.1, 5: 0.2} speed_ratio_map = {1: 0.3, 2: 0.4, 3: 0.1, 5: 0.2}
env = RailEnv(width=50, env = RailEnv(width=50, height=50,
height=50, rail_generator=sparse_rail_generator(), line_generator=sparse_line_generator(),
rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=1, min_dist=8, max_dist=99999,
seed=1),
schedule_generator=complex_schedule_generator(),
number_of_agents=10) number_of_agents=10)
env.reset() env.reset()
actual_speeds = list(map(lambda agent: agent.speed_data['speed'], env.agents)) actual_speeds = list(map(lambda agent: agent.speed_counter.speed, env.agents))
expected_speed_set = set(speed_ratio_map.keys()) expected_speed_set = set(speed_ratio_map.keys())
......
tests/test_utils.py
View file @ a6c4ae6a
...@@ -5,10 +5,15 @@ import numpy as np ...@@ -5,10 +5,15 @@ import numpy as np
from attr import attrs, attrib from attr import attrs, attrib
from flatland.core.grid.grid4 import Grid4TransitionsEnum from flatland.core.grid.grid4 import Grid4TransitionsEnum
from flatland.envs.agent_utils import EnvAgent, RailAgentStatus from flatland.envs.agent_utils import EnvAgent
from flatland.envs.malfunction_generators import MalfunctionParameters, malfunction_from_params
from flatland.envs.rail_env import RailEnvActions, RailEnv from flatland.envs.rail_env import RailEnvActions, RailEnv
from flatland.envs.rail_generators import RailGenerator
from flatland.envs.line_generators import LineGenerator
from flatland.utils.rendertools import RenderTool from flatland.utils.rendertools import RenderTool
from flatland.envs.persistence import RailEnvPersister
from flatland.envs.step_utils.states import TrainState
from flatland.envs.step_utils.speed_counter import SpeedCounter
@attrs @attrs
class Replay(object): class Replay(object):
...@@ -18,7 +23,7 @@ class Replay(object): ...@@ -18,7 +23,7 @@ class Replay(object):
malfunction = attrib(default=0, type=int) malfunction = attrib(default=0, type=int)
set_malfunction = attrib(default=None, type=Optional[int]) set_malfunction = attrib(default=None, type=Optional[int])
reward = attrib(default=None, type=Optional[float]) reward = attrib(default=None, type=Optional[float])
status = attrib(default=None, type=Optional[RailAgentStatus]) state = attrib(default=None, type=Optional[TrainState])
@attrs @attrs
...@@ -38,7 +43,8 @@ def set_penalties_for_replay(env: RailEnv): ...@@ -38,7 +43,8 @@ def set_penalties_for_replay(env: RailEnv):
env.invalid_action_penalty = -29 env.invalid_action_penalty = -29
def run_replay_config(env: RailEnv, test_configs: List[ReplayConfig], rendering: bool = False, activate_agents=True): def run_replay_config(env: RailEnv, test_configs: List[ReplayConfig], rendering: bool = False, activate_agents=True,
skip_reward_check=False, set_ready_to_depart=False, skip_action_required_check=False):
""" """
Runs the replay configs and checks assertions. Runs the replay configs and checks assertions.
...@@ -83,8 +89,19 @@ def run_replay_config(env: RailEnv, test_configs: List[ReplayConfig], rendering: ...@@ -83,8 +89,19 @@ def run_replay_config(env: RailEnv, test_configs: List[ReplayConfig], rendering:
agent.initial_direction = test_config.initial_direction agent.initial_direction = test_config.initial_direction
agent.direction = test_config.initial_direction agent.direction = test_config.initial_direction
agent.target = test_config.target agent.target = test_config.target
agent.speed_data['speed'] = test_config.speed agent.speed_counter = SpeedCounter(speed=test_config.speed)
env.reset(False, False, activate_agents) env.reset(False, False)
if set_ready_to_depart:
# Set all agents to ready to depart
for i_agent in range(len(env.agents)):
env.agents[i_agent].earliest_departure = 0
env.agents[i_agent]._set_state(TrainState.READY_TO_DEPART)
elif activate_agents:
for a_idx in range(len(env.agents)):
env.agents[a_idx].position = env.agents[a_idx].initial_position
env.agents[a_idx]._set_state(TrainState.MOVING)
def _assert(a, actual, expected, msg): def _assert(a, actual, expected, msg):
print("[{}] verifying {} on agent {}: actual={}, expected={}".format(step, msg, a, actual, expected)) print("[{}] verifying {} on agent {}: actual={}, expected={}".format(step, msg, a, actual, expected))
...@@ -98,19 +115,22 @@ def run_replay_config(env: RailEnv, test_configs: List[ReplayConfig], rendering: ...@@ -98,19 +115,22 @@ def run_replay_config(env: RailEnv, test_configs: List[ReplayConfig], rendering:
for a, test_config in enumerate(test_configs): for a, test_config in enumerate(test_configs):
agent: EnvAgent = env.agents[a] agent: EnvAgent = env.agents[a]
replay = test_config.replay[step] replay = test_config.replay[step]
# if not agent.position == replay.position:
# import pdb; pdb.set_trace()
_assert(a, agent.position, replay.position, 'position') _assert(a, agent.position, replay.position, 'position')
_assert(a, agent.direction, replay.direction, 'direction') _assert(a, agent.direction, replay.direction, 'direction')
if replay.status is not None: if replay.state is not None:
_assert(a, agent.status, replay.status, 'status') _assert(a, agent.state, replay.state, 'state')
if replay.action is not None: if replay.action is not None:
assert info_dict['action_required'][ if not skip_action_required_check:
a] == True or agent.status == RailAgentStatus.READY_TO_DEPART, "[{}] agent {} expecting action_required={} or agent status READY_TO_DEPART".format( assert info_dict['action_required'][
a] == True or agent.state == TrainState.READY_TO_DEPART, "[{}] agent {} expecting action_required={} or agent status READY_TO_DEPART".format(
step, a, True) step, a, True)
action_dict[a] = replay.action action_dict[a] = replay.action
else: else:
assert info_dict['action_required'][ if not skip_action_required_check:
assert info_dict['action_required'][
a] == False, "[{}] agent {} expecting action_required={}, but found {}".format( a] == False, "[{}] agent {} expecting action_required={}, but found {}".format(
step, a, False, info_dict['action_required'][a]) step, a, False, info_dict['action_required'][a])
...@@ -118,16 +138,34 @@ def run_replay_config(env: RailEnv, test_configs: List[ReplayConfig], rendering: ...@@ -118,16 +138,34 @@ def run_replay_config(env: RailEnv, test_configs: List[ReplayConfig], rendering:
# As we force malfunctions on the agents we have to set a positive rate that the env # As we force malfunctions on the agents we have to set a positive rate that the env
# recognizes the agent as potentially malfuncitoning # recognizes the agent as potentially malfuncitoning
# We also set next malfunction to infitiy to avoid interference with our tests # We also set next malfunction to infitiy to avoid interference with our tests
agent.malfunction_data['malfunction'] = replay.set_malfunction env.agents[a].malfunction_handler._set_malfunction_down_counter(replay.set_malfunction)
agent.malfunction_data['moving_before_malfunction'] = agent.moving _assert(a, agent.malfunction_handler.malfunction_down_counter, replay.malfunction, 'malfunction')
agent.malfunction_data['fixed'] = False
_assert(a, agent.malfunction_data['malfunction'], replay.malfunction, 'malfunction')
print(step) print(step)
_, rewards_dict, _, info_dict = env.step(action_dict) _, rewards_dict, _, info_dict = env.step(action_dict)
# import pdb; pdb.set_trace()
if rendering: if rendering:
renderer.render_env(show=True, show_observations=True) renderer.render_env(show=True, show_observations=True)
for a, test_config in enumerate(test_configs): for a, test_config in enumerate(test_configs):
replay = test_config.replay[step] replay = test_config.replay[step]
_assert(a, rewards_dict[a], replay.reward, 'reward') if not skip_reward_check:
_assert(a, rewards_dict[a], replay.reward, 'reward')
def create_and_save_env(file_name: str, line_generator: LineGenerator, rail_generator: RailGenerator):
stochastic_data = MalfunctionParameters(malfunction_rate=1000, # Rate of malfunction occurence
min_duration=15, # Minimal duration of malfunction
max_duration=50 # Max duration of malfunction
)
env = RailEnv(width=30,
height=30,
rail_generator=rail_generator,
line_generator=line_generator,
number_of_agents=10,
malfunction_generator_and_process_data=malfunction_from_params(stochastic_data),
remove_agents_at_target=True)
env.reset(True, True)
#env.save(file_name)
RailEnvPersister.save(env, file_name)
return env
tox.ini
View file @ a6c4ae6a
[tox] [tox]
envlist = py36, py37, examples, notebooks, flake8, docs, coverage envlist = py37, py38, examples, docs, coverage
[travis] [travis]
python = python =
3.8: py38
3.7: py37 3.7: py37
3.6: py36
[flake8] [flake8]
max-line-length = 120 max-line-length = 120
ignore = E121 E126 E123 E128 E133 E226 E241 E242 E704 W291 W293 W391 W503 W504 W505 ignore = E121 E126 E123 E128 E133 E226 E241 E242 E704 W291 W293 W391 W503 W504 W505
[testenv:flake8] [testenv:flake8]
basepython = python basepython = python3.7
passenv = DISPLAY passenv = DISPLAY
deps = deps =
-r{toxinidir}/requirements_dev.txt -r{toxinidir}/requirements_dev.txt
...@@ -21,16 +21,13 @@ commands = ...@@ -21,16 +21,13 @@ commands =
flake8 flatland tests examples benchmarks flake8 flatland tests examples benchmarks
[testenv:docs] [testenv:docs]
; use python3.6 because of incompatibility under Windows of the pycairo installed through conda for py37 basepython = python3.7
basepython = python3.6
whitelist_externals = make whitelist_externals = make
passenv = passenv =
DISPLAY DISPLAY
HTTP_PROXY HTTP_PROXY
HTTPS_PROXY HTTPS_PROXY
conda_deps = conda_deps =
cairosvg
pycairo
tk tk
graphviz graphviz
conda_channels : conda_channels :
...@@ -44,8 +41,7 @@ commands = ...@@ -44,8 +41,7 @@ commands =
make docs make docs
[testenv:coverage] [testenv:coverage]
; use python3.6 because of incompatibility under Windows of the pycairo installed through conda for py37 basepython = python3.7
basepython = python3.6
whitelist_externals = make whitelist_externals = make
passenv = passenv =
DISPLAY DISPLAY
...@@ -53,8 +49,6 @@ passenv = ...@@ -53,8 +49,6 @@ passenv =
HTTP_PROXY HTTP_PROXY
HTTPS_PROXY HTTPS_PROXY
conda_deps = conda_deps =
cairosvg
pycairo
tk tk
conda_channels : conda_channels :
conda-forge conda-forge
...@@ -67,8 +61,7 @@ commands = ...@@ -67,8 +61,7 @@ commands =
python make_coverage.py python make_coverage.py
[testenv:benchmarks] [testenv:benchmarks]
; use python3.6 because of incompatibility under Windows of the pycairo installed through conda for py37 basepython = python3.7
basepython = python3.6
setenv = setenv =
PYTHONPATH = {toxinidir} PYTHONPATH = {toxinidir}
passenv = passenv =
...@@ -81,14 +74,13 @@ whitelist_externals = sh ...@@ -81,14 +74,13 @@ whitelist_externals = sh
deps = deps =
-r{toxinidir}/requirements_dev.txt -r{toxinidir}/requirements_dev.txt
-r{toxinidir}/requirements_continuous_integration.txt -r{toxinidir}/requirements_continuous_integration.txt
changedir = {toxinidir} changedir = {toxinidir}
commands = commands =
python --version python --version
python {toxinidir}/benchmarks/benchmark_all_examples.py python {toxinidir}/benchmarks/benchmark_all_examples.py
[testenv:profiling] [testenv:profiling]
; use python3.6 because of incompatibility under Windows of the pycairo installed through conda for py37 basepython = python3.7
basepython = python3.6
setenv = setenv =
PYTHONPATH = {toxinidir} PYTHONPATH = {toxinidir}
passenv = passenv =
...@@ -98,8 +90,6 @@ passenv = ...@@ -98,8 +90,6 @@ passenv =
HTTP_PROXY HTTP_PROXY
HTTPS_PROXY HTTPS_PROXY
conda_deps = conda_deps =
cairosvg
pycairo
tk tk
conda_channels : conda_channels :
conda-forge conda-forge
...@@ -107,14 +97,13 @@ conda_channels : ...@@ -107,14 +97,13 @@ conda_channels :
deps = deps =
-r{toxinidir}/requirements_dev.txt -r{toxinidir}/requirements_dev.txt
-r{toxinidir}/requirements_continuous_integration.txt -r{toxinidir}/requirements_continuous_integration.txt
changedir = {toxinidir} changedir = {toxinidir}
commands = commands =
python {toxinidir}/benchmarks/profile_all_examples.py python {toxinidir}/benchmarks/profile_all_examples.py
[testenv:examples] [testenv:examples]
; TODO should examples be run with py36 and py37?? ; TODO should examples be run with py36 and py37??
; use python3.6 because of incompatibility under Windows of the pycairo installed through conda for py37 basepython = python3.7
basepython = python3.6
setenv = setenv =
PYTHONPATH = {toxinidir} PYTHONPATH = {toxinidir}
passenv = passenv =
...@@ -124,8 +113,6 @@ passenv = ...@@ -124,8 +113,6 @@ passenv =
HTTP_PROXY HTTP_PROXY
HTTPS_PROXY HTTPS_PROXY
conda_deps = conda_deps =
cairosvg
pycairo
tk tk
conda_channels : conda_channels :
conda-forge conda-forge
...@@ -139,10 +126,10 @@ commands = ...@@ -139,10 +126,10 @@ commands =
[testenv:notebooks] [testenv:notebooks]
; TODO should examples be run with py36 and py37?? ; TODO should examples be run with py36 and py37??
; use python3.6 because of incompatibility under Windows of the pycairo installed through conda for py37 basepython = python3.7
basepython = python3.6
setenv = setenv =
PYTHONPATH = {toxinidir} PYTHONPATH = {envdir}
;{toxinidir}
passenv = passenv =
DISPLAY DISPLAY
XAUTHORITY XAUTHORITY
...@@ -150,12 +137,12 @@ passenv = ...@@ -150,12 +137,12 @@ passenv =
HTTP_PROXY HTTP_PROXY
HTTPS_PROXY HTTPS_PROXY
whitelist_externals = sh whitelist_externals = sh
bash
pwd
deps = deps =
-r{toxinidir}/requirements_dev.txt -r{toxinidir}/requirements_dev.txt
-r{toxinidir}/requirements_continuous_integration.txt -r{toxinidir}/requirements_continuous_integration.txt
conda_deps = conda_deps =
cairosvg
pycairo
tk tk
conda_channels : conda_channels :
conda-forge conda-forge
...@@ -163,6 +150,8 @@ conda_channels : ...@@ -163,6 +150,8 @@ conda_channels :
; run tests from subfolder to ensure that resources are accessed via resources and not via relative paths ; run tests from subfolder to ensure that resources are accessed via resources and not via relative paths
changedir = {envtmpdir}/6f59bc68108c3895b1828abdd04b9a06 changedir = {envtmpdir}/6f59bc68108c3895b1828abdd04b9a06
commands = commands =
bash -c "pwd"
bash -c "echo $PYTHONPATH"
python -m jupyter nbextension install --py --sys-prefix widgetsnbextension python -m jupyter nbextension install --py --sys-prefix widgetsnbextension
python -m jupyter nbextension enable --py --sys-prefix widgetsnbextension python -m jupyter nbextension enable --py --sys-prefix widgetsnbextension
python -m jupyter nbextension install --py --sys-prefix jpy_canvas python -m jupyter nbextension install --py --sys-prefix jpy_canvas
...@@ -170,8 +159,7 @@ commands = ...@@ -170,8 +159,7 @@ commands =
python {toxinidir}/notebooks/run_all_notebooks.py python {toxinidir}/notebooks/run_all_notebooks.py
[testenv:start_jupyter] [testenv:start_jupyter]
; use python3.6 because of incompatibility under Windows of the pycairo installed through conda for py37 basepython = python3.7
basepython = python3.6
setenv = setenv =
PYTHONPATH = {toxinidir} PYTHONPATH = {toxinidir}
passenv = passenv =
...@@ -185,8 +173,6 @@ deps = ...@@ -185,8 +173,6 @@ deps =
-r{toxinidir}/requirements_dev.txt -r{toxinidir}/requirements_dev.txt
-r{toxinidir}/requirements_continuous_integration.txt -r{toxinidir}/requirements_continuous_integration.txt
conda_deps = conda_deps =
cairosvg
pycairo
tk tk
conda_channels : conda_channels :
conda-forge conda-forge
...@@ -199,7 +185,9 @@ commands = ...@@ -199,7 +185,9 @@ commands =
python -m jupyter nbextension enable --py --sys-prefix jpy_canvas python -m jupyter nbextension enable --py --sys-prefix jpy_canvas
python -m jupyter notebook python -m jupyter notebook
[testenv] [testenv:py37]
platform = linux|linux2|darwin
basepython = python3.7
setenv = setenv =
PYTHONPATH = {toxinidir} PYTHONPATH = {toxinidir}
passenv = passenv =
...@@ -209,8 +197,6 @@ passenv = ...@@ -209,8 +197,6 @@ passenv =
HTTP_PROXY HTTP_PROXY
HTTPS_PROXY HTTPS_PROXY
conda_deps = conda_deps =
cairosvg
pycairo
tk tk
conda_channels : conda_channels :
conda-forge conda-forge
...@@ -223,9 +209,11 @@ commands = ...@@ -223,9 +209,11 @@ commands =
python --version python --version
python -m pytest --basetemp={envtmpdir} {toxinidir} python -m pytest --basetemp={envtmpdir} {toxinidir}
[testenv:py37]
; exclude py37 from Windows because of incompatibility the pycairo installed through conda for py37
[testenv:py38]
platform = linux|linux2|darwin platform = linux|linux2|darwin
basepython = python3.8
setenv = setenv =
PYTHONPATH = {toxinidir} PYTHONPATH = {toxinidir}
passenv = passenv =
...@@ -235,8 +223,6 @@ passenv = ...@@ -235,8 +223,6 @@ passenv =
HTTP_PROXY HTTP_PROXY
HTTPS_PROXY HTTPS_PROXY
conda_deps = conda_deps =
cairosvg
pycairo
tk tk
conda_channels : conda_channels :
conda-forge conda-forge
......