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)
