def tests_random_interference_from_outside():
"""Tests that malfunctions are produced by stochastic_data!"""
# Set fixed malfunction duration for this test
rail, rail_map = make_simple_rail2()
env = RailEnv(width=25,
height=30,
rail_generator=rail_from_grid_transition_map(rail),
schedule_generator=random_schedule_generator(seed=2),
number_of_agents=1,
random_seed=1)
env.reset()
env.agents[0].speed_data['speed'] = 0.33
env.reset(False, False, False, random_seed=10)
env_data = []
for step in range(200):
action_dict: Dict[int, RailEnvActions] = {}
for agent in env.agents:
# We randomly select an action
action_dict[agent.handle] = RailEnvActions(2)
_, reward, _, _ = env.step(action_dict)
# Append the rewards of the first trial
env_data.append((reward[0], env.agents[0].position))
assert reward[0] == env_data[step][0]
assert env.agents[0].position == env_data[step][1]
# Run the same test as above but with an external random generator running
# Check that the reward stays the same
rail, rail_map = make_simple_rail2()
random.seed(47)
np.random.seed(1234)
env = RailEnv(width=25,
height=30,
rail_generator=rail_from_grid_transition_map(rail),
schedule_generator=random_schedule_generator(seed=2),
number_of_agents=1,
random_seed=1)
env.reset()
env.agents[0].speed_data['speed'] = 0.33
env.reset(False, False, False, random_seed=10)
dummy_list = [1, 2, 6, 7, 8, 9, 4, 5, 4]
for step in range(200):
action_dict: Dict[int, RailEnvActions] = {}
for agent in env.agents:
# We randomly select an action
action_dict[agent.handle] = RailEnvActions(2)
# Do dummy random number generations
random.shuffle(dummy_list)
np.random.rand()
_, reward, _, _ = env.step(action_dict)
assert reward[0] == env_data[step][0]
assert env.agents[0].position == env_data[step][1]
def test_malfunction_values_and_behavior():
"""
Test the malfunction counts down as desired
Returns
-------
"""
# Set fixed malfunction duration for this test
rail, rail_map = make_simple_rail2()
action_dict: Dict[int, RailEnvActions] = {}
stochastic_data = MalfunctionParameters(malfunction_rate=0.001, # Rate of malfunction occurence
min_duration=10, # Minimal duration of malfunction
max_duration=10 # Max duration of malfunction
)
env = RailEnv(width=25,
height=30,
rail_generator=rail_from_grid_transition_map(rail),
schedule_generator=random_schedule_generator(),
number_of_agents=1,
malfunction_generator_and_process_data=malfunction_from_params(stochastic_data),
obs_builder_object=SingleAgentNavigationObs()
)
env.reset(False, False, activate_agents=True, random_seed=10)
# Assertions
assert_list = [9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 10, 9, 8, 7, 6, 5]
print("[")
for time_step in range(15):
# Move in the env
env.step(action_dict)
# Check that next_step decreases as expected
assert env.agents[0].malfunction_data['malfunction'] == assert_list[time_step]
def test_malfunction_before_entry():
"""Tests that malfunctions are working properly for agents before entering the environment!"""
# Set fixed malfunction duration for this test
stochastic_data = MalfunctionParameters(malfunction_rate=2, # Rate of malfunction occurence
min_duration=10, # Minimal duration of malfunction
max_duration=10 # Max duration of malfunction
)
rail, rail_map = make_simple_rail2()
env = RailEnv(width=25,
height=30,
rail_generator=rail_from_grid_transition_map(rail),
schedule_generator=random_schedule_generator(),
number_of_agents=10,
malfunction_generator_and_process_data=malfunction_from_params(stochastic_data),
obs_builder_object=SingleAgentNavigationObs()
)
env.reset(False, False, False, random_seed=10)
env.agents[0].target = (0, 0)
# Test initial malfunction values for all agents
# we want some agents to be malfuncitoning already and some to be working
# we want different next_malfunction values for the agents
assert env.agents[0].malfunction_data['malfunction'] == 0
assert env.agents[1].malfunction_data['malfunction'] == 10
assert env.agents[2].malfunction_data['malfunction'] == 0
assert env.agents[3].malfunction_data['malfunction'] == 10
assert env.agents[4].malfunction_data['malfunction'] == 10
assert env.agents[5].malfunction_data['malfunction'] == 10
assert env.agents[6].malfunction_data['malfunction'] == 10
assert env.agents[7].malfunction_data['malfunction'] == 10
assert env.agents[8].malfunction_data['malfunction'] == 10
assert env.agents[9].malfunction_data['malfunction'] == 10
def test_malfanction_from_params():
"""
Test loading malfunction from
Returns
-------
"""
stochastic_data = MalfunctionParameters(
malfunction_rate=1000, # Rate of malfunction occurence
min_duration=2, # Minimal duration of malfunction
max_duration=5 # Max duration of malfunction
)
rail, rail_map = make_simple_rail2()
env = RailEnv(
width=25,
height=30,
rail_generator=rail_from_grid_transition_map(rail),
schedule_generator=random_schedule_generator(),
number_of_agents=10,
malfunction_generator_and_process_data=malfunction_from_params(
stochastic_data))
env.reset()
assert env.malfunction_process_data.malfunction_rate == 1000
assert env.malfunction_process_data.min_duration == 2
assert env.malfunction_process_data.max_duration == 5
def test_single_malfunction_generator():
"""
Test single malfunction generator
Returns
-------
"""
rail, rail_map = make_simple_rail2()
env = RailEnv(
width=25,
height=30,
rail_generator=rail_from_grid_transition_map(rail),
schedule_generator=random_schedule_generator(),
number_of_agents=10,
malfunction_generator_and_process_data=single_malfunction_generator(
earlierst_malfunction=10, malfunction_duration=5))
for test in range(10):
env.reset()
action_dict = dict()
tot_malfunctions = 0
print(test)
for i in range(10):
for agent in env.agents:
# Go forward all the time
action_dict[agent.handle] = RailEnvActions(2)
env.step(action_dict)
for agent in env.agents:
# Go forward all the time
tot_malfunctions += agent.malfunction_data['nr_malfunctions']
assert tot_malfunctions == 1
def test_seeding_and_observations():
# Test if two different instances diverge with different observations
rail, rail_map = make_simple_rail2()
# Make two seperate envs with different observation builders
# Global Observation
env = RailEnv(width=25,
height=30,
rail_generator=rail_from_grid_transition_map(rail),
schedule_generator=random_schedule_generator(seed=12),
number_of_agents=10,
obs_builder_object=GlobalObsForRailEnv())
# Tree Observation
env2 = RailEnv(width=25,
height=30,
rail_generator=rail_from_grid_transition_map(rail),
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)
# Check that both environments produce the same initial start positions
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[2].initial_position == env2.agents[2].initial_position
assert env.agents[3].initial_position == env2.agents[3].initial_position
assert env.agents[4].initial_position == env2.agents[4].initial_position
assert env.agents[5].initial_position == env2.agents[5].initial_position
assert env.agents[6].initial_position == env2.agents[6].initial_position
assert env.agents[7].initial_position == env2.agents[7].initial_position
assert env.agents[8].initial_position == env2.agents[8].initial_position
assert env.agents[9].initial_position == env2.agents[9].initial_position
action_dict = {}
for step in range(10):
for a in range(env.get_num_agents()):
action = np.random.randint(4)
action_dict[a] = action
env.step(action_dict)
env2.step(action_dict)
# Check that both environments end up in the same position
assert env.agents[0].position == env2.agents[0].position
assert env.agents[1].position == env2.agents[1].position
assert env.agents[2].position == env2.agents[2].position
assert env.agents[3].position == env2.agents[3].position
assert env.agents[4].position == env2.agents[4].position
assert env.agents[5].position == env2.agents[5].position
assert env.agents[6].position == env2.agents[6].position
assert env.agents[7].position == env2.agents[7].position
assert env.agents[8].position == env2.agents[8].position
assert env.agents[9].position == env2.agents[9].position
for a in range(env.get_num_agents()):
print("assert env.agents[{}].position == env2.agents[{}].position".
format(a, a))
def test_random_seeding():
# Set fixed malfunction duration for this test
rail, rail_map = make_simple_rail2()
# Move target to unreachable position in order to not interfere with test
for idx in range(100):
env = RailEnv(width=25,
height=30,
rail_generator=rail_from_grid_transition_map(rail),
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)
for step in range(10):
actions = {}
actions[0] = 2
env.step(actions)
agent_positions = []
env.agents[0].initial_position == (3, 2)
env.agents[1].initial_position == (3, 5)
env.agents[2].initial_position == (3, 6)
env.agents[3].initial_position == (5, 6)
env.agents[4].initial_position == (3, 4)
env.agents[5].initial_position == (3, 1)
env.agents[6].initial_position == (3, 9)
env.agents[7].initial_position == (4, 6)
env.agents[8].initial_position == (0, 3)
env.agents[9].initial_position == (3, 7)
def test_malfunction_process():
# Set fixed malfunction duration for this test
stochastic_data = MalfunctionParameters(
malfunction_rate=1, # Rate of malfunction occurence
min_duration=3, # Minimal duration of malfunction
max_duration=3 # Max duration of malfunction
)
rail, rail_map = make_simple_rail2()
env = RailEnv(
width=25,
height=30,
rail_generator=rail_from_grid_transition_map(rail),
schedule_generator=random_schedule_generator(),
number_of_agents=1,
malfunction_generator_and_process_data=malfunction_from_params(
stochastic_data),
obs_builder_object=SingleAgentNavigationObs())
obs, info = env.reset(False, False, True, random_seed=10)
agent_halts = 0
total_down_time = 0
agent_old_position = env.agents[0].position
# Move target to unreachable position in order to not interfere with test
env.agents[0].target = (0, 0)
for step in range(100):
actions = {}
for i in range(len(obs)):
actions[i] = np.argmax(obs[i]) + 1
obs, all_rewards, done, _ = env.step(actions)
if env.agents[0].malfunction_data['malfunction'] > 0:
agent_malfunctioning = True
else:
agent_malfunctioning = False
if agent_malfunctioning:
# Check that agent is not moving while malfunctioning
assert agent_old_position == env.agents[0].position
agent_old_position = env.agents[0].position
total_down_time += env.agents[0].malfunction_data['malfunction']
# Check that the appropriate number of malfunctions is achieved
assert env.agents[0].malfunction_data[
'nr_malfunctions'] == 23, "Actual {}".format(
env.agents[0].malfunction_data['nr_malfunctions'])
# Check that malfunctioning data was standing around
assert total_down_time > 0
def test_malfunction_process_statistically():
"""Tests that malfunctions are produced by stochastic_data!"""
# Set fixed malfunction duration for this test
stochastic_data = MalfunctionParameters(
malfunction_rate=1 / 5, # Rate of malfunction occurence
min_duration=5, # Minimal duration of malfunction
max_duration=5 # Max duration of malfunction
)
rail, rail_map = make_simple_rail2()
env = RailEnv(
width=25,
height=30,
rail_generator=rail_from_grid_transition_map(rail),
schedule_generator=random_schedule_generator(),
number_of_agents=10,
malfunction_generator_and_process_data=malfunction_from_params(
stochastic_data),
obs_builder_object=SingleAgentNavigationObs())
env.reset(True, True, False, random_seed=10)
env.agents[0].target = (0, 0)
# Next line only for test generation
# agent_malfunction_list = [[] for i in range(10)]
agent_malfunction_list = [
[0, 0, 0, 0, 5, 4, 3, 2, 1, 0, 5, 4, 3, 2, 1, 0, 0, 0, 5, 4],
[0, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 5, 4, 3, 2, 1, 0, 5, 4, 3, 2, 1, 0, 0, 5, 4, 3, 2],
[0, 0, 0, 0, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 5, 4, 3, 2, 1],
[0, 0, 5, 4, 3, 2, 1, 0, 0, 5, 4, 3, 2, 1, 0, 5, 4, 3, 2, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0],
[5, 4, 3, 2, 1, 0, 0, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0, 5],
[5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 4, 3, 2],
[5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 5, 4, 3, 2, 1, 0, 0, 0, 5, 4]
]
for step in range(20):
action_dict: Dict[int, RailEnvActions] = {}
for agent_idx in range(env.get_num_agents()):
# We randomly select an action
action_dict[agent_idx] = RailEnvActions(np.random.randint(4))
# For generating tests only:
# agent_malfunction_list[agent_idx].append(env.agents[agent_idx].malfunction_data['malfunction'])
assert env.agents[agent_idx].malfunction_data[
'malfunction'] == agent_malfunction_list[agent_idx][step]
env.step(action_dict)
def test_last_malfunction_step():
"""
Test to check that agent moves when it is not malfunctioning
"""
# Set fixed malfunction duration for this test
rail, rail_map = make_simple_rail2()
env = RailEnv(width=25,
height=30,
rail_generator=rail_from_grid_transition_map(rail),
schedule_generator=random_schedule_generator(seed=2),
number_of_agents=1,
random_seed=1)
env.reset()
env.agents[0].speed_data['speed'] = 1. / 3.
env.agents[0].target = (0, 0)
env.reset(False, False, True)
# Force malfunction to be off at beginning and next malfunction to happen in 2 steps
env.agents[0].malfunction_data['next_malfunction'] = 2
env.agents[0].malfunction_data['malfunction'] = 0
env_data = []
for step in range(20):
action_dict: Dict[int, RailEnvActions] = {}
for agent in env.agents:
# Go forward all the time
action_dict[agent.handle] = RailEnvActions(2)
if env.agents[0].malfunction_data['malfunction'] < 1:
agent_can_move = True
# Store the position before and after the step
pre_position = env.agents[0].speed_data['position_fraction']
_, reward, _, _ = env.step(action_dict)
# Check if the agent is still allowed to move in this step
if env.agents[0].malfunction_data['malfunction'] > 0:
agent_can_move = False
post_position = env.agents[0].speed_data['position_fraction']
# Assert that the agent moved while it was still allowed
if agent_can_move:
assert pre_position != post_position
else:
assert post_position == pre_position
def test_initial_malfunction_do_nothing():
stochastic_data = MalfunctionParameters(malfunction_rate=70, # Rate of malfunction occurence
min_duration=2, # Minimal duration of malfunction
max_duration=5 # Max duration of malfunction
)
rail, rail_map = make_simple_rail2()
env = RailEnv(width=25,
height=30,
rail_generator=rail_from_grid_transition_map(rail),
schedule_generator=random_schedule_generator(),
number_of_agents=1,
malfunction_generator_and_process_data=malfunction_from_params(stochastic_data),
# Malfunction data generator
)
env.reset()
set_penalties_for_replay(env)
replay_config = ReplayConfig(
replay=[
Replay(
position=None,
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.MOVE_FORWARD,
set_malfunction=3,
malfunction=3,
reward=env.step_penalty, # full step penalty while malfunctioning
status=RailAgentStatus.READY_TO_DEPART
),
Replay(
position=(3, 2),
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.DO_NOTHING,
malfunction=2,
reward=env.step_penalty, # full step penalty while malfunctioning
status=RailAgentStatus.ACTIVE
),
# malfunction stops in the next step and we're still at the beginning of the cell
# --> if we take action DO_NOTHING, agent should restart without moving
#
Replay(
position=(3, 2),
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.DO_NOTHING,
malfunction=1,
reward=env.step_penalty, # full step penalty while stopped
status=RailAgentStatus.ACTIVE
),
# we haven't started moving yet --> stay here
Replay(
position=(3, 2),
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.DO_NOTHING,
malfunction=0,
reward=env.step_penalty, # full step penalty while stopped
status=RailAgentStatus.ACTIVE
),
Replay(
position=(3, 2),
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.MOVE_FORWARD,
malfunction=0,
reward=env.start_penalty + env.step_penalty * 1.0, # start penalty + step penalty for speed 1.0
status=RailAgentStatus.ACTIVE
), # we start to move forward --> should go to next cell now
Replay(
position=(3, 3),
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.MOVE_FORWARD,
malfunction=0,
reward=env.step_penalty * 1.0, # step penalty for speed 1.0
status=RailAgentStatus.ACTIVE
)
],
speed=env.agents[0].speed_data['speed'],
target=env.agents[0].target,
initial_position=(3, 2),
initial_direction=Grid4TransitionsEnum.EAST,
)
run_replay_config(env, [replay_config], activate_agents=False)
def test_initial_malfunction_stop_moving():
rail, rail_map = make_simple_rail2()
env = RailEnv(width=25, height=30, rail_generator=rail_from_grid_transition_map(rail),
schedule_generator=random_schedule_generator(), number_of_agents=1,
obs_builder_object=SingleAgentNavigationObs())
env.reset()
print(env.agents[0].initial_position, env.agents[0].direction, env.agents[0].position, env.agents[0].status)
set_penalties_for_replay(env)
replay_config = ReplayConfig(
replay=[
Replay(
position=None,
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.MOVE_FORWARD,
set_malfunction=3,
malfunction=3,
reward=env.step_penalty, # full step penalty when stopped
status=RailAgentStatus.READY_TO_DEPART
),
Replay(
position=(3, 2),
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.DO_NOTHING,
malfunction=2,
reward=env.step_penalty, # full step penalty when stopped
status=RailAgentStatus.ACTIVE
),
# malfunction stops in the next step and we're still at the beginning of the cell
# --> if we take action STOP_MOVING, agent should restart without moving
#
Replay(
position=(3, 2),
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.STOP_MOVING,
malfunction=1,
reward=env.step_penalty, # full step penalty while stopped
status=RailAgentStatus.ACTIVE
),
# we have stopped and do nothing --> should stand still
Replay(
position=(3, 2),
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.DO_NOTHING,
malfunction=0,
reward=env.step_penalty, # full step penalty while stopped
status=RailAgentStatus.ACTIVE
),
# we start to move forward --> should go to next cell now
Replay(
position=(3, 2),
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.MOVE_FORWARD,
malfunction=0,
reward=env.start_penalty + env.step_penalty * 1.0, # full step penalty while stopped
status=RailAgentStatus.ACTIVE
),
Replay(
position=(3, 3),
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.MOVE_FORWARD,
malfunction=0,
reward=env.step_penalty * 1.0, # full step penalty while stopped
status=RailAgentStatus.ACTIVE
)
],
speed=env.agents[0].speed_data['speed'],
target=env.agents[0].target,
initial_position=(3, 2),
initial_direction=Grid4TransitionsEnum.EAST,
)
run_replay_config(env, [replay_config], activate_agents=False)
def test_initial_malfunction():
stochastic_data = MalfunctionParameters(malfunction_rate=1000, # Rate of malfunction occurence
min_duration=2, # Minimal duration of malfunction
max_duration=5 # Max duration of malfunction
)
rail, rail_map = make_simple_rail2()
env = RailEnv(width=25,
height=30,
rail_generator=rail_from_grid_transition_map(rail),
schedule_generator=random_schedule_generator(seed=10),
number_of_agents=1,
malfunction_generator_and_process_data=malfunction_from_params(stochastic_data),
# Malfunction data generator
obs_builder_object=SingleAgentNavigationObs()
)
# reset to initialize agents_static
env.reset(False, False, True, random_seed=10)
print(env.agents[0].malfunction_data)
env.agents[0].target = (0, 5)
set_penalties_for_replay(env)
replay_config = ReplayConfig(
replay=[
Replay(
position=(3, 2),
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.MOVE_FORWARD,
set_malfunction=3,
malfunction=3,
reward=env.step_penalty # full step penalty when malfunctioning
),
Replay(
position=(3, 2),
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.MOVE_FORWARD,
malfunction=2,
reward=env.step_penalty # full step penalty when malfunctioning
),
# malfunction stops in the next step and we're still at the beginning of the cell
# --> if we take action MOVE_FORWARD, agent should restart and move to the next cell
Replay(
position=(3, 2),
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.MOVE_FORWARD,
malfunction=1,
reward=env.step_penalty
), # malfunctioning ends: starting and running at speed 1.0
Replay(
position=(3, 2),
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.MOVE_FORWARD,
malfunction=0,
reward=env.start_penalty + env.step_penalty * 1.0 # running at speed 1.0
),
Replay(
position=(3, 3),
direction=Grid4TransitionsEnum.EAST,
action=RailEnvActions.MOVE_FORWARD,
malfunction=0,
reward=env.step_penalty # running at speed 1.0
)
],
speed=env.agents[0].speed_data['speed'],
target=env.agents[0].target,
initial_position=(3, 2),
initial_direction=Grid4TransitionsEnum.EAST,
)
run_replay_config(env, [replay_config])
def test_dummy_predictor(rendering=False):
rail, rail_map = make_simple_rail2()
env = RailEnv(
width=rail_map.shape[1],
height=rail_map.shape[0],
rail_generator=rail_from_grid_transition_map(rail),
schedule_generator=random_schedule_generator(),
number_of_agents=1,
obs_builder_object=TreeObsForRailEnv(
max_depth=2, predictor=DummyPredictorForRailEnv(max_depth=10)),
)
env.reset()
# set initial position and direction for testing...
env.agents[0].initial_position = (5, 6)
env.agents[0].initial_direction = 0
env.agents[0].direction = 0
env.agents[0].target = (3, 0)
env.reset(False, False)
env.set_agent_active(env.agents[0])
if rendering:
renderer = RenderTool(env, gl="PILSVG")
renderer.render_env(show=True, show_observations=False)
input("Continue?")
# test assertions
predictions = env.obs_builder.predictor.get(None)
positions = np.array(
list(map(lambda prediction: [*prediction[1:3]], predictions[0])))
directions = np.array(
list(map(lambda prediction: [prediction[3]], predictions[0])))
time_offsets = np.array(
list(map(lambda prediction: [prediction[0]], predictions[0])))
actions = np.array(
list(map(lambda prediction: [prediction[4]], predictions[0])))
# compare against expected values
expected_positions = np.array([
[5., 6.],
[4., 6.],
[3., 6.],
[3., 5.],
[3., 4.],
[3., 3.],
[3., 2.],
[3., 1.],
# at target (3,0): stay in this position from here on
[3., 0.],
[3., 0.],
[3., 0.],
])
expected_directions = np.array([
[0.],
[0.],
[0.],
[3.],
[3.],
[3.],
[3.],
[3.],
# at target (3,0): stay in this position from here on
[3.],
[3.],
[3.]
])
expected_time_offsets = np.array([
[0.],
[1.],
[2.],
[3.],
[4.],
[5.],
[6.],
[7.],
[8.],
[9.],
[10.],
])
expected_actions = np.array([
[0.],
[2.],
[2.],
[2.],
[2.],
[2.],
[2.],
[2.],
# reaching target by straight
[2.],
# at target: stopped moving
[4.],
[4.],
])
assert np.array_equal(positions, expected_positions)
assert np.array_equal(directions, expected_directions)
assert np.array_equal(time_offsets, expected_time_offsets)
assert np.array_equal(actions, expected_actions)
def test_seeding_and_malfunction():
# Test if two different instances diverge with different observations
rail, rail_map = make_simple_rail2()
stochastic_data = {
'prop_malfunction': 0.4,
'malfunction_rate': 2,
'min_duration': 10,
'max_duration': 10
}
# Make two seperate envs with different and see if the exhibit the same malfunctions
# Global Observation
for tests in range(1, 100):
env = RailEnv(width=25,
height=30,
rail_generator=rail_from_grid_transition_map(rail),
schedule_generator=random_schedule_generator(),
number_of_agents=10,
obs_builder_object=GlobalObsForRailEnv())
# Tree Observation
env2 = RailEnv(width=25,
height=30,
rail_generator=rail_from_grid_transition_map(rail),
schedule_generator=random_schedule_generator(),
number_of_agents=10,
obs_builder_object=GlobalObsForRailEnv())
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
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[2].initial_position == env2.agents[
2].initial_position
assert env.agents[3].initial_position == env2.agents[
3].initial_position
assert env.agents[4].initial_position == env2.agents[
4].initial_position
assert env.agents[5].initial_position == env2.agents[
5].initial_position
assert env.agents[6].initial_position == env2.agents[
6].initial_position
assert env.agents[7].initial_position == env2.agents[
7].initial_position
assert env.agents[8].initial_position == env2.agents[
8].initial_position
assert env.agents[9].initial_position == env2.agents[
9].initial_position
action_dict = {}
for step in range(10):
for a in range(env.get_num_agents()):
action = np.random.randint(4)
action_dict[a] = action
# print("----------------------")
# print(env.agents[a].malfunction_data, env.agents[a].status)
# print(env2.agents[a].malfunction_data, env2.agents[a].status)
_, reward1, done1, _ = env.step(action_dict)
_, reward2, done2, _ = env2.step(action_dict)
for a in range(env.get_num_agents()):
assert reward1[a] == reward2[a]
assert done1[a] == done2[a]
# Check that both environments end up in the same position
assert env.agents[0].position == env2.agents[0].position
assert env.agents[1].position == env2.agents[1].position
assert env.agents[2].position == env2.agents[2].position
assert env.agents[3].position == env2.agents[3].position
assert env.agents[4].position == env2.agents[4].position
assert env.agents[5].position == env2.agents[5].position
assert env.agents[6].position == env2.agents[6].position
assert env.agents[7].position == env2.agents[7].position
assert env.agents[8].position == env2.agents[8].position
assert env.agents[9].position == env2.agents[9].position
