Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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93 行
3.8 KiB

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