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41 行
1.8 KiB
41 行
1.8 KiB
import numpy as np
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from mlagents.trainers.buffer import AgentBuffer
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from mlagents_envs.base_env import BehaviorSpec
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from mlagents.trainers.trajectory import SplitObservations
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def create_agent_buffer(
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behavior_spec: BehaviorSpec, number: int, reward: float = 0.0
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) -> AgentBuffer:
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buffer = AgentBuffer()
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curr_observations = [
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np.random.normal(size=shape).astype(np.float32)
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for shape in behavior_spec.observation_shapes
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]
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next_observations = [
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np.random.normal(size=shape).astype(np.float32)
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for shape in behavior_spec.observation_shapes
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]
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action_buffer = behavior_spec.action_spec.random_action(1)
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action = {}
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if behavior_spec.action_spec.continuous_size > 0:
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action["continuous_action"] = action_buffer.continuous
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if behavior_spec.action_spec.discrete_size > 0:
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action["discrete_action"] = action_buffer.discrete
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for _ in range(number):
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curr_split_obs = SplitObservations.from_observations(curr_observations)
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next_split_obs = SplitObservations.from_observations(next_observations)
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for i, _ in enumerate(curr_split_obs.visual_observations):
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buffer["visual_obs%d" % i].append(curr_split_obs.visual_observations[i])
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buffer["next_visual_obs%d" % i].append(
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next_split_obs.visual_observations[i]
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)
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buffer["vector_obs"].append(curr_split_obs.vector_observations)
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buffer["next_vector_in"].append(next_split_obs.vector_observations)
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for _act_type, _act in action.items():
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buffer[_act_type].append(_act[0, :])
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buffer["reward"].append(np.ones(1, dtype=np.float32) * reward)
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buffer["masks"].append(np.ones(1, dtype=np.float32))
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buffer["done"] = np.zeros(number, dtype=np.float32)
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return buffer
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