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56 行
2.5 KiB
56 行
2.5 KiB
# # Unity ML-Agents Toolkit
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# ## ML-Agent Learning (Behavioral Cloning)
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# Contains an implementation of Behavioral Cloning Algorithm
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import logging
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import copy
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from mlagents.trainers.bc.trainer import BCTrainer
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from mlagents.trainers.demo_loader import demo_to_buffer
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from mlagents.trainers.trainer import UnityTrainerException
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logger = logging.getLogger("mlagents.trainers")
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class OfflineBCTrainer(BCTrainer):
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"""The OfflineBCTrainer is an implementation of Offline Behavioral Cloning."""
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def __init__(self, brain, trainer_parameters, training, load, seed, run_id):
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"""
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Responsible for collecting experiences and training PPO model.
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:param trainer_parameters: The parameters for the trainer (dictionary).
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:param training: Whether the trainer is set for training.
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:param load: Whether the model should be loaded.
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:param seed: The seed the model will be initialized with
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:param run_id: The The identifier of the current run
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"""
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super(OfflineBCTrainer, self).__init__(
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brain, trainer_parameters, training, load, seed, run_id)
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self.param_keys = ['batch_size', 'summary_freq', 'max_steps',
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'batches_per_epoch', 'use_recurrent',
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'hidden_units', 'learning_rate', 'num_layers',
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'sequence_length', 'memory_size', 'model_path',
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'demo_path']
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self.check_param_keys()
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self.batches_per_epoch = trainer_parameters['batches_per_epoch']
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self.n_sequences = max(int(trainer_parameters['batch_size'] / self.policy.sequence_length),
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1)
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brain_params, self.demonstration_buffer = demo_to_buffer(
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trainer_parameters['demo_path'],
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self.policy.sequence_length)
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policy_brain = copy.deepcopy(brain.__dict__)
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expert_brain = copy.deepcopy(brain_params.__dict__)
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policy_brain.pop('brain_name')
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expert_brain.pop('brain_name')
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if expert_brain != policy_brain:
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raise UnityTrainerException("The provided demonstration is not compatible with the "
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"brain being used for performance evaluation.")
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def __str__(self):
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return '''Hyperparameters for the Imitation Trainer of brain {0}: \n{1}'''.format(
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self.brain_name, '\n'.join(
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['\t{0}:\t{1}'.format(x, self.trainer_parameters[x]) for x in self.param_keys]))
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