def test_batched_step_result_from_proto_raises_on_nan():
n_agents = 10
shapes = [(3,), (4,)]
group_spec = AgentGroupSpec(shapes, ActionType.CONTINUOUS, 3)
ap_list = generate_list_agent_proto(n_agents, shapes, nan_observations=True)
with pytest.raises(RuntimeError):
batched_step_result_from_proto(ap_list, group_spec)
def make_demo_buffer(
pair_infos: List[AgentInfoActionPairProto],
group_spec: AgentGroupSpec,
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_step_info = batched_step_result_from_proto(
[current_pair_info.agent_info], group_spec)
next_step_info = batched_step_result_from_proto(
[next_pair_info.agent_info], group_spec)
previous_action = (np.array(pair_infos[idx].action_info.vector_actions,
dtype=np.float32) * 0)
if idx > 0:
previous_action = np.array(
pair_infos[idx - 1].action_info.vector_actions,
dtype=np.float32)
curr_agent_id = current_step_info.agent_id[0]
current_agent_step_info = current_step_info.get_agent_step_result(
curr_agent_id)
next_agent_id = next_step_info.agent_id[0]
next_agent_step_info = next_step_info.get_agent_step_result(
next_agent_id)
demo_raw_buffer["done"].append(next_agent_step_info.done)
demo_raw_buffer["rewards"].append(next_agent_step_info.reward)
split_obs = SplitObservations.from_observations(
current_agent_step_info.obs)
for i, obs in enumerate(split_obs.visual_observations):
demo_raw_buffer["visual_obs%d" % i].append(obs)
demo_raw_buffer["vector_obs"].append(split_obs.vector_observations)
demo_raw_buffer["actions"].append(
current_pair_info.action_info.vector_actions)
demo_raw_buffer["prev_action"].append(previous_action)
if next_step_info.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
def test_action_masking_continuous():
n_agents = 10
shapes = [(3, ), (4, )]
group_spec = AgentGroupSpec(shapes, ActionType.CONTINUOUS, 10)
ap_list = generate_list_agent_proto(n_agents, shapes)
result = batched_step_result_from_proto(ap_list, group_spec)
masks = result.action_mask
assert masks is None
def test_action_masking_discrete_1():
n_agents = 10
shapes = [(3, ), (4, )]
group_spec = AgentGroupSpec(shapes, ActionType.DISCRETE, (10, ))
ap_list = generate_list_agent_proto(n_agents, shapes)
result = batched_step_result_from_proto(ap_list, group_spec)
masks = result.action_mask
assert isinstance(masks, list)
assert len(masks) == 1
assert masks[0].shape == (n_agents, 10)
assert masks[0][0, 0]
def test_batched_step_result_from_proto():
n_agents = 10
shapes = [(3, ), (4, )]
group_spec = AgentGroupSpec(shapes, ActionType.CONTINUOUS, 3)
ap_list = generate_list_agent_proto(n_agents, shapes)
result = batched_step_result_from_proto(ap_list, group_spec)
assert list(result.reward) == list(range(n_agents))
assert list(result.agent_id) == list(range(n_agents))
for index in range(n_agents):
assert result.done[index] == (index % 2 == 0)
assert result.max_step[index] == (index % 2 == 1)
assert list(result.obs[0].shape) == [n_agents] + list(shapes[0])
assert list(result.obs[1].shape) == [n_agents] + list(shapes[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] = batched_step_result_from_proto(
agent_info_list, self._env_specs[brain_name])
else:
self._env_state[brain_name] = BatchedStepResult.empty(
self._env_specs[brain_name])
self._parse_side_channel_message(self.side_channels,
output.side_channel)
