您最多选择25个主题
主题必须以中文或者字母或数字开头，可以包含连字符 (-)，并且长度不得超过35个字符
106 行
4.0 KiB
106 行
4.0 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:
|
|
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
|