def _generate_step_input(
self, vector_action: Dict[str, ActionTuple]
) -> UnityInputProto:
rl_in = UnityRLInputProto()
for b in vector_action:
n_agents = len(self._env_state[b][0])
if n_agents == 0:
continue
for i in range(n_agents):
action = AgentActionProto()
if vector_action[b].continuous is not None:
action.vector_actions_deprecated.extend(
vector_action[b].continuous[i]
)
action.continuous_actions.extend(vector_action[b].continuous[i])
if vector_action[b].discrete is not None:
action.vector_actions_deprecated.extend(
vector_action[b].discrete[i]
)
action.discrete_actions.extend(vector_action[b].discrete[i])
rl_in.agent_actions[b].value.extend([action])
rl_in.command = STEP
rl_in.side_channel = bytes(
self._side_channel_manager.generate_side_channel_messages()
)
return self._wrap_unity_input(rl_in)
def proto_from_steps_and_action(
decision_steps: DecisionSteps,
terminal_steps: TerminalSteps,
continuous_actions: np.ndarray,
discrete_actions: np.ndarray,
) -> List[AgentInfoActionPairProto]:
agent_info_protos = proto_from_steps(decision_steps, terminal_steps)
agent_action_protos = []
num_agents = (len(continuous_actions)
if continuous_actions is not None else len(discrete_actions))
for i in range(num_agents):
proto = AgentActionProto()
if continuous_actions is not None:
proto.continuous_actions.extend(continuous_actions[i])
proto.vector_actions_deprecated.extend(continuous_actions[i])
if discrete_actions is not None:
proto.discrete_actions.extend(discrete_actions[i])
proto.vector_actions_deprecated.extend(discrete_actions[i])
agent_action_protos.append(proto)
agent_info_action_pair_protos = [
AgentInfoActionPairProto(agent_info=agent_info_proto,
action_info=action_proto)
for agent_info_proto, action_proto in zip(agent_info_protos,
agent_action_protos)
]
return agent_info_action_pair_protos
def _generate_step_input(
self, vector_action: Dict[str, np.ndarray]) -> UnityInputProto:
rl_in = UnityRLInputProto()
for b in vector_action:
n_agents = len(self._env_state[b][0])
if n_agents == 0:
continue
for i in range(n_agents):
action = AgentActionProto(vector_actions=vector_action[b][i])
rl_in.agent_actions[b].value.extend([action])
rl_in.command = STEP
rl_in.side_channel = bytes(
self._generate_side_channel_data(self.side_channels))
return self.wrap_unity_input(rl_in)
def proto_from_steps_and_action(
decision_steps: DecisionSteps, terminal_steps: TerminalSteps,
actions: np.ndarray) -> List[AgentInfoActionPairProto]:
agent_info_protos = proto_from_steps(decision_steps, terminal_steps)
agent_action_protos = [
AgentActionProto(vector_actions=action) for action in actions
]
agent_info_action_pair_protos = [
AgentInfoActionPairProto(agent_info=agent_info_proto,
action_info=action_proto)
for agent_info_proto, action_proto in zip(agent_info_protos,
agent_action_protos)
]
return agent_info_action_pair_protos
def proto_from_batched_step_result_and_action(
batched_step_result: BatchedStepResult, actions: np.ndarray
) -> List[AgentInfoActionPairProto]:
agent_info_protos = proto_from_batched_step_result(batched_step_result)
agent_action_protos = [
AgentActionProto(vector_actions=action) for action in actions
]
agent_info_action_pair_protos = [
AgentInfoActionPairProto(agent_info=agent_info_proto, action_info=action_proto)
for agent_info_proto, action_proto in zip(
agent_info_protos, agent_action_protos
)
]
return agent_info_action_pair_protos
