def test_batched_step_result_from_proto():
n_agents = 10
shapes = [(3, ), (4, )]
spec = BehaviorSpec(create_observation_specs_with_shapes(shapes),
ActionSpec.create_continuous(3))
ap_list = generate_list_agent_proto(n_agents, shapes)
decision_steps, terminal_steps = steps_from_proto(ap_list, spec)
for agent_id in range(n_agents):
if agent_id in decision_steps:
# we set the reward equal to the agent id in generate_list_agent_proto
assert decision_steps[agent_id].reward == agent_id
elif agent_id in terminal_steps:
assert terminal_steps[agent_id].reward == agent_id
else:
raise Exception("Missing agent from the steps")
# We sort the AgentId since they are split between DecisionSteps and TerminalSteps
combined_agent_id = list(decision_steps.agent_id) + list(
terminal_steps.agent_id)
combined_agent_id.sort()
assert combined_agent_id == list(range(n_agents))
for agent_id in range(n_agents):
assert (agent_id in terminal_steps) == (agent_id % 2 == 0)
if agent_id in terminal_steps:
assert terminal_steps[agent_id].interrupted == (agent_id % 4 == 0)
assert decision_steps.obs[0].shape[1] == shapes[0][0]
assert decision_steps.obs[1].shape[1] == shapes[1][0]
assert terminal_steps.obs[0].shape[1] == shapes[0][0]
assert terminal_steps.obs[1].shape[1] == shapes[1][0]
def test_action_masking_continuous():
n_agents = 10
shapes = [(3, ), (4, )]
behavior_spec = BehaviorSpec(shapes, ActionType.CONTINUOUS, 10)
ap_list = generate_list_agent_proto(n_agents, shapes)
decision_steps, terminal_steps = steps_from_proto(ap_list, behavior_spec)
masks = decision_steps.action_mask
assert masks is None
def test_action_masking_continuous():
n_agents = 10
shapes = [(3, ), (4, )]
behavior_spec = BehaviorSpec(create_observation_specs_with_shapes(shapes),
ActionSpec.create_continuous(10))
ap_list = generate_list_agent_proto(n_agents, shapes)
decision_steps, terminal_steps = steps_from_proto(ap_list, behavior_spec)
masks = decision_steps.action_mask
assert masks is None
def test_action_masking_discrete_1():
n_agents = 10
shapes = [(3, ), (4, )]
behavior_spec = BehaviorSpec(shapes, ActionType.DISCRETE, (10, ))
ap_list = generate_list_agent_proto(n_agents, shapes)
decision_steps, terminal_steps = steps_from_proto(ap_list, behavior_spec)
masks = decision_steps.action_mask
assert isinstance(masks, list)
assert len(masks) == 1
assert masks[0].shape == (n_agents / 2, 10)
assert masks[0][0, 0]
def test_action_masking_discrete_2():
n_agents = 10
shapes = [(3, ), (4, )]
behavior_spec = BehaviorSpec(shapes, ActionSpec.create_discrete((2, 2, 6)))
ap_list = generate_list_agent_proto(n_agents, shapes)
decision_steps, terminal_steps = steps_from_proto(ap_list, behavior_spec)
masks = decision_steps.action_mask
assert isinstance(masks, list)
assert len(masks) == 3
assert masks[0].shape == (n_agents / 2, 2)
assert masks[1].shape == (n_agents / 2, 2)
assert masks[2].shape == (n_agents / 2, 6)
assert masks[0][0, 0]
def test_action_masking_discrete():
n_agents = 10
shapes = [(3, ), (4, )]
behavior_spec = BehaviorSpec(create_observation_specs_with_shapes(shapes),
ActionSpec.create_discrete((7, 3)))
ap_list = generate_list_agent_proto(n_agents, shapes)
decision_steps, terminal_steps = steps_from_proto(ap_list, behavior_spec)
masks = decision_steps.action_mask
assert isinstance(masks, list)
assert len(masks) == 2
assert masks[0].shape == (n_agents / 2, 7) # half agents are done
assert masks[1].shape == (n_agents / 2, 3) # half agents are done
assert masks[0][0, 0]
assert not masks[1][0, 0]
assert masks[1][0, 1]
def _update_state(self, output: UnityRLOutputProto) -> None:
"""
Collects experience information from all external brains in environment at current step.
"""
for brain_name in self._env_specs.keys():
if brain_name in output.agentInfos:
agent_info_list = output.agentInfos[brain_name].value
self._env_state[brain_name] = steps_from_proto(
agent_info_list, self._env_specs[brain_name]
)
else:
self._env_state[brain_name] = (
DecisionSteps.empty(self._env_specs[brain_name]),
TerminalSteps.empty(self._env_specs[brain_name]),
)
self._side_channel_manager.process_side_channel_message(output.side_channel)
def make_demo_buffer(
pair_infos: List[AgentInfoActionPairProto],
behavior_spec: BehaviorSpec,
sequence_length: int,
) -> AgentBuffer:
# Create and populate buffer using experiences
demo_raw_buffer = AgentBuffer()
demo_processed_buffer = AgentBuffer()
for idx, current_pair_info in enumerate(pair_infos):
if idx > len(pair_infos) - 2:
break
next_pair_info = pair_infos[idx + 1]
current_decision_step, current_terminal_step = steps_from_proto(
[current_pair_info.agent_info], behavior_spec
)
next_decision_step, next_terminal_step = steps_from_proto(
[next_pair_info.agent_info], behavior_spec
)
previous_action = (
np.array(
pair_infos[idx].action_info.vector_actions_deprecated, dtype=np.float32
)
* 0
)
if idx > 0:
previous_action = np.array(
pair_infos[idx - 1].action_info.vector_actions_deprecated,
dtype=np.float32,
)
next_done = len(next_terminal_step) == 1
next_reward = 0
if len(next_terminal_step) == 1:
next_reward = next_terminal_step.reward[0]
else:
next_reward = next_decision_step.reward[0]
current_obs = None
if len(current_terminal_step) == 1:
current_obs = list(current_terminal_step.values())[0].obs
else:
current_obs = list(current_decision_step.values())[0].obs
demo_raw_buffer["done"].append(next_done)
demo_raw_buffer["rewards"].append(next_reward)
for i, obs in enumerate(current_obs):
demo_raw_buffer[ObsUtil.get_name_at(i)].append(obs)
if (
len(current_pair_info.action_info.continuous_actions) == 0
and len(current_pair_info.action_info.discrete_actions) == 0
):
if behavior_spec.action_spec.continuous_size > 0:
demo_raw_buffer["continuous_action"].append(
current_pair_info.action_info.vector_actions_deprecated
)
else:
demo_raw_buffer["discrete_action"].append(
current_pair_info.action_info.vector_actions_deprecated
)
else:
if behavior_spec.action_spec.continuous_size > 0:
demo_raw_buffer["continuous_action"].append(
current_pair_info.action_info.continuous_actions
)
if behavior_spec.action_spec.discrete_size > 0:
demo_raw_buffer["discrete_action"].append(
current_pair_info.action_info.discrete_actions
)
demo_raw_buffer["prev_action"].append(previous_action)
if next_done:
demo_raw_buffer.resequence_and_append(
demo_processed_buffer, batch_size=None, training_length=sequence_length
)
demo_raw_buffer.reset_agent()
demo_raw_buffer.resequence_and_append(
demo_processed_buffer, batch_size=None, training_length=sequence_length
)
return demo_processed_buffer
