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97 行
3.7 KiB
97 行
3.7 KiB
from .communicator import Communicator
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from mlagents.envs.communicator_objects.unity_rl_output_pb2 import UnityRLOutputProto
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from mlagents.envs.communicator_objects.brain_parameters_pb2 import BrainParametersProto
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from mlagents.envs.communicator_objects.unity_rl_initialization_output_pb2 import (
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UnityRLInitializationOutputProto,
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)
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from mlagents.envs.communicator_objects.unity_input_pb2 import UnityInputProto
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from mlagents.envs.communicator_objects.unity_output_pb2 import UnityOutputProto
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from mlagents.envs.communicator_objects.resolution_pb2 import ResolutionProto
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from mlagents.envs.communicator_objects.agent_info_pb2 import AgentInfoProto
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class MockCommunicator(Communicator):
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def __init__(
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self,
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discrete_action=False,
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visual_inputs=0,
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stack=True,
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num_agents=3,
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brain_name="RealFakeBrain",
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vec_obs_size=3,
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):
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"""
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Python side of the grpc communication. Python is the client and Unity the server
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:int base_port: Baseline port number to connect to Unity environment over. worker_id increments over this.
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:int worker_id: Number to add to communication port (5005) [0]. Used for asynchronous agent scenarios.
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"""
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self.is_discrete = discrete_action
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self.steps = 0
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self.visual_inputs = visual_inputs
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self.has_been_closed = False
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self.num_agents = num_agents
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self.brain_name = brain_name
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self.vec_obs_size = vec_obs_size
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if stack:
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self.num_stacks = 2
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else:
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self.num_stacks = 1
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def initialize(self, inputs: UnityInputProto) -> UnityOutputProto:
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resolutions = [
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ResolutionProto(width=30, height=40, gray_scale=False)
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for i in range(self.visual_inputs)
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]
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bp = BrainParametersProto(
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vector_observation_size=self.vec_obs_size,
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num_stacked_vector_observations=self.num_stacks,
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vector_action_size=[2],
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camera_resolutions=resolutions,
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vector_action_descriptions=["", ""],
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vector_action_space_type=int(not self.is_discrete),
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brain_name=self.brain_name,
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is_training=True,
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)
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rl_init = UnityRLInitializationOutputProto(
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name="RealFakeAcademy", version="API-10", log_path="", brain_parameters=[bp]
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)
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return UnityOutputProto(rl_initialization_output=rl_init)
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def exchange(self, inputs: UnityInputProto) -> UnityOutputProto:
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dict_agent_info = {}
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if self.is_discrete:
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vector_action = [1]
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else:
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vector_action = [1, 2]
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list_agent_info = []
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if self.num_stacks == 1:
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observation = [1, 2, 3]
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else:
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observation = [1, 2, 3, 1, 2, 3]
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for i in range(self.num_agents):
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list_agent_info.append(
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AgentInfoProto(
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stacked_vector_observation=observation,
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reward=1,
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stored_vector_actions=vector_action,
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stored_text_actions="",
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text_observation="",
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memories=[],
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done=(i == 2),
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max_step_reached=False,
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id=i,
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)
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)
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dict_agent_info["RealFakeBrain"] = UnityRLOutputProto.ListAgentInfoProto(
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value=list_agent_info
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)
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result = UnityRLOutputProto(agentInfos=dict_agent_info)
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return UnityOutputProto(rl_output=result)
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def close(self):
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"""
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Sends a shutdown signal to the unity environment, and closes the grpc connection.
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"""
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self.has_been_closed = True
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