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93 行
3.8 KiB
93 行
3.8 KiB
import logging
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import numpy as np
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from mlagents.trainers.bc.models import BehavioralCloningModel
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from mlagents.trainers.policy import Policy
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logger = logging.getLogger("mlagents.trainers")
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class BCPolicy(Policy):
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def __init__(self, seed, brain, trainer_parameters, load):
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"""
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:param seed: Random seed.
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:param brain: Assigned Brain object.
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:param trainer_parameters: Defined training parameters.
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:param load: Whether a pre-trained model will be loaded or a new one created.
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"""
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super(BCPolicy, self).__init__(seed, brain, trainer_parameters)
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with self.graph.as_default():
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with self.graph.as_default():
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self.model = BehavioralCloningModel(
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h_size=int(trainer_parameters['hidden_units']),
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lr=float(trainer_parameters['learning_rate']),
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n_layers=int(trainer_parameters['num_layers']),
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m_size=self.m_size,
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normalize=False,
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use_recurrent=trainer_parameters['use_recurrent'],
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brain=brain,
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seed=seed)
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if load:
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self._load_graph()
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else:
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self._initialize_graph()
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self.inference_dict = {'action': self.model.sample_action}
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self.update_dict = {'policy_loss': self.model.loss,
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'update_batch': self.model.update}
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if self.use_recurrent:
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self.inference_dict['memory_out'] = self.model.memory_out
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self.evaluate_rate = 1.0
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self.update_rate = 0.5
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def evaluate(self, brain_info):
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"""
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Evaluates policy for the agent experiences provided.
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:param brain_info: BrainInfo input to network.
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:return: Results of evaluation.
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"""
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feed_dict = {self.model.dropout_rate: self.evaluate_rate,
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self.model.sequence_length: 1}
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feed_dict = self._fill_eval_dict(feed_dict, brain_info)
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if self.use_recurrent:
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if brain_info.memories.shape[1] == 0:
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brain_info.memories = self.make_empty_memory(len(brain_info.agents))
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feed_dict[self.model.memory_in] = brain_info.memories
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run_out = self._execute_model(feed_dict, self.inference_dict)
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return run_out
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def update(self, mini_batch, num_sequences):
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"""
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Performs update on model.
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:param mini_batch: Batch of experiences.
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:param num_sequences: Number of sequences to process.
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:return: Results of update.
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"""
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feed_dict = {self.model.dropout_rate: self.update_rate,
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self.model.batch_size: num_sequences,
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self.model.sequence_length: self.sequence_length}
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if self.use_continuous_act:
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feed_dict[self.model.true_action] = mini_batch['actions']. \
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reshape([-1, self.brain.vector_action_space_size[0]])
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else:
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feed_dict[self.model.true_action] = mini_batch['actions'].reshape(
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[-1, len(self.brain.vector_action_space_size)])
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feed_dict[self.model.action_masks] = np.ones(
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(num_sequences, sum(self.brain.vector_action_space_size)))
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if self.use_vec_obs:
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apparent_obs_size = self.brain.vector_observation_space_size * \
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self.brain.num_stacked_vector_observations
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feed_dict[self.model.vector_in] = mini_batch['vector_obs'] \
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.reshape([-1,apparent_obs_size])
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for i, _ in enumerate(self.model.visual_in):
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visual_obs = mini_batch['visual_obs%d' % i]
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feed_dict[self.model.visual_in[i]] = visual_obs
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if self.use_recurrent:
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feed_dict[self.model.memory_in] = np.zeros([num_sequences, self.m_size])
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run_out = self._execute_model(feed_dict, self.update_dict)
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return run_out
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