def build_dummy_maze_env_with_structured_core_env() -> DummyEnvironment:
"""
Instantiates the DummyEnvironment.
:return: Instance of a DummyEnvironment
"""
observation_conversion = ObservationConversion()
return DummyEnvironment(core_env=DummyStructuredCoreEnvironment(
observation_conversion.space()),
action_conversion=[DictActionConversion()],
observation_conversion=[observation_conversion])
def test_return_normalization_wrapper():
""" Unit tests """
observation_conversion = ObservationConversion()
env = DummyEnvironment(
core_env=DummyCoreEnvironment(observation_conversion.space()),
action_conversion=[DictActionConversion()],
observation_conversion=[observation_conversion])
env = ReturnNormalizationRewardWrapper(env, gamma=0.99, epsilon=1e-8)
env.reset()
reward = env.step(env.action_space.sample())[1]
assert isinstance(reward, float)
assert not hasattr(reward, 'shape')
def build_dummy_structured_environment() -> DummyStructuredEnvironment:
"""
Instantiates the DummyStructuredEnvironment.
:return: Instance of a DummyStructuredEnvironment
"""
observation_conversion = PreProcessingObservationConversion()
maze_env = DummyEnvironment(
core_env=DummyCoreEnvironment(observation_conversion.space()),
action_conversion=[DictActionConversion()],
observation_conversion=[observation_conversion])
return DummyStructuredEnvironment(maze_env=maze_env)
def test_sorted_action_spaces_wrapper():
""" Unit tests """
observation_conversion = ObservationConversion()
env = DummyEnvironment(core_env=DummyCoreEnvironment(
observation_conversion.space()),
action_conversion=[DictActionConversion()],
observation_conversion=[observation_conversion])
env = AddActionWrapper.wrap(env)
env = SortedSpacesWrapper.wrap(env)
sorted_action_space = env.action_space
assert list(sorted_action_space.spaces.keys()) == list(
sorted(sorted_action_space.spaces.keys()))
def test_reward_clipping_wrapper():
""" Unit tests """
observation_conversion = ObservationConversion()
env = DummyEnvironment(core_env=DummyCoreEnvironment(
observation_conversion.space()),
action_conversion=[DictActionConversion()],
observation_conversion=[observation_conversion])
env.reset()
action = env.action_space.sample()
np.random.seed(1234)
wrapped_env = RewardClippingWrapper(env, min_val=-0.1, max_val=0.1)
np.random.seed(1234)
wrapped_reward = wrapped_env.step(action)[1]
assert -0.1 <= wrapped_reward <= 0.1
def test_reward_scaling_wrapper():
""" Unit tests """
observation_conversion = ObservationConversion()
env = DummyEnvironment(core_env=DummyCoreEnvironment(
observation_conversion.space()),
action_conversion=[DictActionConversion()],
observation_conversion=[observation_conversion])
env.reset()
action = env.action_space.sample()
np.random.seed(1234)
original_reward = env.step(action)[1]
wrapped_env = RewardScalingWrapper(env, scale=0.1)
np.random.seed(1234)
wrapped_reward = wrapped_env.step(action)[1]
assert original_reward == wrapped_reward * 10
def test_sorted_observation_spaces_wrapper():
""" Unit tests """
observation_conversion = ObservationConversion()
env = DummyEnvironment(core_env=DummyCoreEnvironment(
observation_conversion.space()),
action_conversion=[DictActionConversion()],
observation_conversion=[observation_conversion])
env = AddObservationWrapper.wrap(env)
env = SortedSpacesWrapper.wrap(env)
sorted_obs_space = env.observation_space
assert list(sorted_obs_space.spaces.keys()) == list(
sorted(sorted_obs_space.spaces.keys()))
_, _ = env.get_observation_and_action_dicts({},
env.last_maze_action,
first_step_in_episode=True)
def test_skipping_wrapper_and_reward_aggregation():
""" Step skipping unit test """
observation_conversion = ObservationConversion()
env = DummyEnvironment(
core_env=DummyCoreEnvironment(observation_conversion.space()),
action_conversion=[DictActionConversion()],
observation_conversion=[observation_conversion]
)
n_steps = 3
env = StepSkipWrapper.wrap(env, n_steps=n_steps, skip_mode='sticky')
env.reset()
for _ in range(4):
action = env.action_space.sample()
obs, reward, done, info = env.step(action)
assert(reward == n_steps*10)
def test_records_once_per_maze_step_in_multistep_envs():
"""In multi-step envs, trajectory should be recorded once per Maze env step (not in each sub-step)."""
observation_conversion = ObservationConversion()
maze_env = DummyEnvironment(
core_env=DummyCoreEnvironment(observation_conversion.space()),
action_conversion=[DictActionConversion()],
observation_conversion=[observation_conversion]
)
env = DummyStructuredEnvironment(maze_env)
class TestWriter(TrajectoryWriter):
"""Mock writer checking the recorded data"""
def __init__(self):
self.episode_count = 0
self.step_count = 0
def write(self, episode_record: StateTrajectoryRecord):
"""Count steps and episodes"""
self.episode_count += 1
self.step_count += len(episode_record.step_records)
writer = TestWriter()
TrajectoryWriterRegistry.writers = [] # Ensure there is no other writer
TrajectoryWriterRegistry.register_writer(writer)
env = TrajectoryRecordingWrapper.wrap(env)
for _ in range(5):
env.reset()
for i in range(10):
env.step(env.action_space.sample())
# final env reset required
env.reset()
env.close()
# The step count should correspond to core env steps (disregarding sub-steps),
# I.e., 5 flat steps per 10 structured + 1 final state for each episode
assert writer.step_count == 5 * (5 + 1)
assert writer.episode_count == 5
def test_records_maze_states_and_actions():
class CustomDummyRewardAggregator(RewardAggregator):
"""Customized dummy reward aggregator subscribed to BaseEnvEvents."""
def get_interfaces(self) -> List[Type[ABC]]:
"""
Return events class is subscribed to.
"""
additional_interfaces: List[Type[ABC]] = [BaseEnvEvents]
parent_interfaces = super().get_interfaces()
return additional_interfaces + parent_interfaces
class CustomDummyCoreEnv(DummyCoreEnvironment):
"""
Customized dummy core env with serializable components that regenerates state only in step.
"""
def __init__(self, observation_space):
super().__init__(observation_space)
self.reward_aggregator = CustomDummyRewardAggregator()
self.maze_state = self.observation_space.sample()
self.pubsub: Pubsub = Pubsub(self.context.event_service)
self.pubsub.register_subscriber(self.reward_aggregator)
self.base_event_publisher = self.pubsub.create_event_topic(BaseEnvEvents)
self.renderer = DummyRenderer()
def get_renderer(self) -> DummyRenderer:
"""
Returns DummyRenderer.
:return: DummyRenderer.
"""
return self.renderer
def step(self, maze_action):
"""
Steps through the environment.
"""
self.maze_state = self.observation_space.sample()
self.base_event_publisher.reward(10)
return super().step(maze_action)
def get_maze_state(self):
"""
Returns current state.
"""
return self.maze_state
def get_serializable_components(self) -> Dict[str, Any]:
"""
Returns minimal dict. with components to serialize.
"""
return {"value": 0}
class TestWriter(TrajectoryWriter):
"""Mock writer checking the recorded data"""
def __init__(self):
self.episode_count = 0
self.step_count = 0
self.episode_records = []
def write(self, episode_record: StateTrajectoryRecord):
"""Count steps and episodes & check instance types"""
self.episode_records.append(episode_record)
self.episode_count += 1
self.step_count += len(episode_record.step_records)
for step_record in episode_record.step_records[:-1]:
assert isinstance(step_record.maze_state, dict)
assert isinstance(step_record.maze_action, dict)
assert step_record.serializable_components != {}
assert len(step_record.step_event_log.events) > 0
final_state_record = episode_record.step_records[-1]
assert isinstance(final_state_record.maze_state, dict)
assert final_state_record.maze_action is None
assert final_state_record.serializable_components != {}
assert isinstance(episode_record.renderer, Renderer)
writer = TestWriter()
TrajectoryWriterRegistry.writers = [] # Ensure there is no other writer
TrajectoryWriterRegistry.register_writer(writer)
# env = env_instantiation_example.example_1()
observation_conversion = ObservationConversion()
env = DummyEnvironment(
core_env=CustomDummyCoreEnv(observation_conversion.space()),
action_conversion=[DictActionConversion()],
observation_conversion=[observation_conversion]
)
env = TrajectoryRecordingWrapper.wrap(env)
policy = DummyGreedyPolicy()
states = [] # Observe changes in states over time.
for _ in range(5):
obs = env.reset()
for _ in range(10):
maze_state = env.get_maze_state()
states.append(deepcopy(maze_state))
obs, _, _, _ = env.step(policy.compute_action(observation=obs, maze_state=maze_state, deterministic=True))
# final env reset required
env.reset()
assert writer.step_count == 5 * (10 + 1) # Count also the recorded final state
assert writer.episode_count == 5
# Compare if the recorded inventory changes from the first episode match with the trajectory records
for step_id in range(10):
assert np.all(
(states[step_id][key] == writer.episode_records[0].step_records[step_id].maze_state[key])
for key in env.observation_conversion.space().spaces
)
def test_raises_exception_on_invalid_type():
with pytest.raises(AssertionError):
registry = Factory(base_type=DummyObservationConversion)
registry.instantiate(config=DictActionConversion())
def test_logs_events():
class CustomDummyKPICalculator(KpiCalculator):
"""
Dummy KPIs for dummy environment.
"""
def calculate_kpis(self, episode_event_log: EpisodeEventLog,
last_maze_state: MazeStateType) -> Dict[str, float]:
"""
Returns a dummy KPI.
"""
return {"dummy_kpi": random.random()}
class CustomDummyRewardAggregator(RewardAggregator):
"""
Customized dummy reward aggregator subscribed to BaseEnvEvents.
"""
def get_interfaces(self) -> List[Type[ABC]]:
"""
Return events class is subscribed to.
"""
additional_interfaces: List[Type[ABC]] = [BaseEnvEvents]
parent_interfaces = super().get_interfaces()
return additional_interfaces + parent_interfaces
class CustomDummyCoreEnv(DummyCoreEnvironment):
"""
Customized dummy core env with serializable components that regenerates state only in step.
"""
def __init__(self, observation_space):
super().__init__(observation_space)
self.reward_aggregator = CustomDummyRewardAggregator()
self.pubsub: Pubsub = Pubsub(self.context.event_service)
self.pubsub.register_subscriber(self.reward_aggregator)
self.base_event_publisher = self.pubsub.create_event_topic(
BaseEnvEvents)
self.kpi_calculator = CustomDummyKPICalculator()
def get_kpi_calculator(self) -> CustomDummyKPICalculator:
"""KPIs are supported."""
return self.kpi_calculator
class TestWriter(LogEventsWriter):
"""Mock writer checking the logged events"""
def __init__(self):
self.episode_count = 0
self.step_count = 0
def write(self, episode_record: EpisodeEventLog):
"""Count steps and episodes & check that some events were logged in each step"""
self.episode_count += 1
self.step_count += len(episode_record.step_event_logs)
for step_id, step_event_log in enumerate(
episode_record.step_event_logs):
assert step_id == step_event_log.env_time
assert len(step_event_log.events) > 0
last_step_event_log = episode_record.step_event_logs[-1]
kpi_count_in_last_step = len(
list(last_step_event_log.events.query_events(
BaseEnvEvents.kpi)))
assert kpi_count_in_last_step != 0
# KPIs should be a part of the last step record only
assert kpi_count_in_last_step == len(
list(episode_record.query_events(BaseEnvEvents.kpi)))
observation_conversion = ObservationConversion()
writer = TestWriter()
_run_rollout_loop(env=DummyEnvironment(
core_env=CustomDummyCoreEnv(observation_conversion.space()),
action_conversion=[DictActionConversion()],
observation_conversion=[observation_conversion]),
n_episodes=5,
n_steps_per_episode=10,
writer=writer)
assert writer.episode_count == 5
assert writer.step_count == 5 * 10
def test_records_once_per_maze_step_in_multistep_envs():
"""In multi-step envs, events should be recorded once per Maze env step (not in each sub-step)."""
class _SubStepEvents(ABC):
@define_epoch_stats(sum)
@define_episode_stats(len)
@define_step_stats(len)
def sub_step_event(self):
"""Dispatched in each sub step."""
class _CoreEnvEvents(ABC):
@define_epoch_stats(sum)
@define_episode_stats(len)
@define_step_stats(len)
def core_env_step_event(self):
"""Dispatched when core env steps (only once per step)."""
class EventDummyEnv(DummyCoreEnvironment):
"""Dummy core env, dispatches one step event in every step."""
def __init__(self, observation_space: gym.spaces.space.Space):
super().__init__(observation_space)
self.dummy_events = self.pubsub.create_event_topic(_CoreEnvEvents)
def step(
self, maze_action: Dict
) -> Tuple[Dict[str, np.ndarray], float, bool, Optional[Dict]]:
"""Dispatch the step event..."""
self.dummy_events.core_env_step_event()
return super().step(maze_action)
class DummyMultiStepEnv(DummyStructuredEnvironment):
"""Simulates simple two-step env. The underlying env is stepped only in the second step."""
def __init__(self, maze_env):
super().__init__(maze_env)
self.dummy_events = self.pubsub.create_event_topic(_SubStepEvents)
def _action0(
self, action
) -> Tuple[Dict, float, bool, Optional[Dict[str, np.ndarray]]]:
self.dummy_events.sub_step_event()
return {}, 0, False, None
def _action1(
self, action
) -> Tuple[Dict, float, bool, Optional[Dict[str, np.ndarray]]]:
self.dummy_events.sub_step_event()
return self.maze_env.step(action)
class TestWriter(LogEventsWriter):
"""Testing writer. Keeps the episode event record."""
def __init__(self):
self.episode_record = None
def write(self, episode_record: EpisodeEventLog):
"""Store the record"""
self.episode_record = episode_record
# Init the env hierarchy
observation_conversion = ObservationConversion()
maze_env = DummyEnvironment(
core_env=EventDummyEnv(observation_conversion.space()),
action_conversion=[DictActionConversion()],
observation_conversion=[observation_conversion])
env = DummyMultiStepEnv(maze_env)
# Run the rollout
writer = TestWriter()
_run_rollout_loop(env=env,
n_episodes=1,
n_steps_per_episode=10,
writer=writer)
# There should be one core env step event and two substep events recorded in every step.
assert writer.episode_record is not None
for step_id, step_event_log in enumerate(
writer.episode_record.step_event_logs):
if (step_id + 1) % 2 == 0:
assert len(
step_event_log.events.query_events(
_CoreEnvEvents.core_env_step_event)) == 1
assert len(
step_event_log.events.query_events(
_SubStepEvents.sub_step_event)) == 2
assert step_event_log.env_time == step_id