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22 行
756 B
22 行
756 B
from typing import Dict
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from mlagents.trainers.buffer import AgentBuffer
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class Optimizer(object):
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"""
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Creates loss functions and auxillary networks (e.g. Q or Value) needed for training.
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Provides methods to update the Policy.
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"""
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def __init__(self):
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self.reward_signals = {}
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def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
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"""
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Update the Policy based on the batch that was passed in.
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:param batch: AgentBuffer that contains the minibatch of data used for this update.
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:param num_sequences: Number of recurrent sequences found in the minibatch.
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:return: A Dict containing statistics (name, value) from the update (e.g. loss)
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"""
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pass
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