def _get_agent_infos(self):
dict_agent_info = {}
list_agent_info = []
vector_obs = [1, 2, 3]
observations = [
ObservationProto(
compressed_data=None,
shape=[30, 40, 3],
compression_type=COMPRESSION_TYPE_PNG,
)
for _ in range(self.visual_inputs)
]
vector_obs_proto = ObservationProto(
float_data=ObservationProto.FloatData(data=vector_obs),
shape=[len(vector_obs)],
compression_type=COMPRESSION_TYPE_NONE,
)
observations.append(vector_obs_proto)
for i in range(self.num_agents):
list_agent_info.append(
AgentInfoProto(
reward=1,
done=(i == 2),
max_step_reached=False,
id=i,
observations=observations,
)
)
dict_agent_info["RealFakeBrain"] = UnityRLOutputProto.ListAgentInfoProto(
value=list_agent_info
)
return dict_agent_info
def initialize(self, inputs: UnityInputProto) -> UnityOutputProto:
bp = BrainParametersProto(
vector_action_size=[2],
vector_action_descriptions=["", ""],
vector_action_space_type=discrete if self.is_discrete else continuous,
brain_name=self.brain_name,
is_training=True,
)
rl_init = UnityRLInitializationOutputProto(
name="RealFakeAcademy",
version=UnityEnvironment.API_VERSION,
log_path="",
brain_parameters=[bp],
)
output = UnityRLOutputProto(agentInfos=self._get_agent_infos())
return UnityOutputProto(rl_initialization_output=rl_init, rl_output=output)
def _get_agent_infos(self):
dict_agent_info = {}
if self.is_discrete:
vector_action = [1]
else:
vector_action = [1, 2]
list_agent_info = []
if self.num_stacks == 1:
observation = [1, 2, 3]
else:
observation = [1, 2, 3, 1, 2, 3]
compressed_obs = [
CompressedObservationProto(
data=None,
shape=[30, 40, 3],
compression_type=CompressionTypeProto.PNG)
for _ in range(self.visual_inputs)
]
for i in range(self.num_agents):
list_agent_info.append(
AgentInfoProto(
stacked_vector_observation=observation,
reward=1,
stored_vector_actions=vector_action,
stored_text_actions="",
text_observation="",
memories=[],
done=(i == 2),
max_step_reached=False,
id=i,
compressed_observations=compressed_obs,
))
dict_agent_info[
"RealFakeBrain"] = UnityRLOutputProto.ListAgentInfoProto(
value=list_agent_info)
return dict_agent_info
def exchange(self, inputs: UnityInputProto) -> UnityOutputProto:
result = UnityRLOutputProto(agentInfos=self._get_agent_infos())
return UnityOutputProto(rl_output=result)