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94 行
3.3 KiB
94 行
3.3 KiB
from mlagents.envs.communicator import Communicator
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from mlagents.envs.communicator_objects import UnityMessage, UnityOutput, UnityInput,\
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ResolutionProto, BrainParametersProto, UnityRLInitializationOutput,\
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AgentInfoProto, UnityRLOutput
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class MockCommunicator(Communicator):
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def __init__(self, discrete_action=False, visual_inputs=0, stack=True, num_agents=3):
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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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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: UnityInput) -> UnityOutput:
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resolutions = [ResolutionProto(
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width=30,
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height=40,
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gray_scale=False) for i in range(self.visual_inputs)]
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bp = BrainParametersProto(
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vector_observation_size=3,
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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="RealFakeBrain",
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brain_type=2
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)
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rl_init = UnityRLInitializationOutput(
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name="RealFakeAcademy",
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version="API-4",
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log_path="",
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brain_parameters=[bp]
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)
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return UnityOutput(
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rl_initialization_output=rl_init
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)
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def exchange(self, inputs: UnityInput) -> UnityOutput:
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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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dict_agent_info["RealFakeBrain"] = \
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UnityRLOutput.ListAgentInfoProto(value=list_agent_info)
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global_done = False
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try:
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global_done = (inputs.rl_input.agent_actions["RealFakeBrain"].value[0].vector_actions[0] == -1)
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except:
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pass
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result = UnityRLOutput(
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global_done=global_done,
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agentInfos=dict_agent_info
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)
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return UnityOutput(
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rl_output=result
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)
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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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