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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3.5 KiB

import logging
import numpy as np
from mlagents.trainers.bc.models import BehavioralCloningModel
from mlagents.trainers.tf_policy import TFPolicy
logger = logging.getLogger("mlagents.trainers")
class BCPolicy(TFPolicy):
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:
feed_dict[self.model.memory_in] = self.retrieve_memories(brain_info.agents)
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"]
else:
feed_dict[self.model.true_action] = mini_batch["actions"]
feed_dict[self.model.action_masks] = np.ones(
(num_sequences, sum(self.brain.vector_action_space_size)),
dtype=np.float32,
)
if self.use_vec_obs:
feed_dict[self.model.vector_in] = mini_batch["vector_obs"]
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], dtype=np.float32
)
run_out = self._execute_model(feed_dict, self.update_dict)
return run_out