def exchange(self, inputs: UnityInput) -> UnityOutput:
dict_agent_info = {}
if self.is_discrete:
vector_action = [1]
else:
vector_action = [1, 2]
list_agent_info = []
for i in range(3):
list_agent_info.append(
AgentInfoProto(stacked_vector_observation=[1, 2, 3, 1, 2, 3],
reward=1,
stored_vector_actions=vector_action,
stored_text_actions="",
text_observation="",
memories=[],
done=(i == 2),
max_step_reached=False,
id=i))
dict_agent_info["RealFakeBrain"] = \
UnityRLOutput.ListAgentInfoProto(value=list_agent_info)
global_done = False
try:
global_done = (inputs.rl_input.agent_actions["RealFakeBrain"].
value[0].vector_actions[0] == -1)
except:
pass
result = UnityRLOutput(global_done=global_done,
agentInfos=dict_agent_info)
return UnityOutput(rl_output=result)
def initialize(self, inputs: UnityInput) -> UnityOutput:
resolutions = [
ResolutionProto(width=30, height=40, gray_scale=False)
for i in range(self.visual_inputs)
]
bp = BrainParametersProto(
vector_observation_size=3,
num_stacked_vector_observations=self.num_stacks,
vector_action_size=[2],
camera_resolutions=resolutions,
vector_action_descriptions=["", ""],
vector_action_space_type=int(not self.is_discrete),
brain_name="RealFakeBrain",
brain_type=2)
rl_init = UnityRLInitializationOutput(name="RealFakeAcademy",
version="API-4",
log_path="",
brain_parameters=[bp])
return UnityOutput(rl_initialization_output=rl_init)