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31 行
1.1 KiB
31 行
1.1 KiB
from mlagents.trainers.brain import BrainParameters, CameraResolution
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from mlagents_envs.base_env import BehaviorSpec
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import numpy as np
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from typing import List
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def behavior_spec_to_brain_parameters(
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name: str, behavior_spec: BehaviorSpec
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) -> BrainParameters:
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vec_size = np.sum(
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[shape[0] for shape in behavior_spec.observation_shapes if len(shape) == 1]
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)
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vis_sizes = [shape for shape in behavior_spec.observation_shapes if len(shape) == 3]
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cam_res = [CameraResolution(s[0], s[1], s[2]) for s in vis_sizes]
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a_size: List[int] = []
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if behavior_spec.is_action_discrete():
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a_size += list(behavior_spec.discrete_action_branches)
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vector_action_space_type = 0
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else:
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a_size += [behavior_spec.action_size]
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vector_action_space_type = 1
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return BrainParameters(
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name, int(vec_size), cam_res, a_size, [], vector_action_space_type
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)
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def get_global_agent_id(worker_id: int, agent_id: int) -> str:
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"""
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Create an agent id that is unique across environment workers using the worker_id.
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"""
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return f"${worker_id}-{agent_id}"
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