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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270 行
9.7 KiB

import logging
from typing import Any, Dict
import numpy as np
from mlagents.trainers import tf
from mlagents.envs.exception import UnityException
from mlagents.envs.policy import Policy
from mlagents.envs.action_info import ActionInfo
from tensorflow.python.platform import gfile
from tensorflow.python.framework import graph_util
from mlagents.trainers import tensorflow_to_barracuda as tf2bc
from mlagents.envs.brain import BrainInfo
logger = logging.getLogger("mlagents.trainers")
class UnityPolicyException(UnityException):
"""
Related to errors with the Trainer.
"""
pass
class TFPolicy(Policy):
"""
Contains a learning model, and the necessary
functions to interact with it to perform evaluate and updating.
"""
possible_output_nodes = [
"action",
"value_estimate",
"action_probs",
"recurrent_out",
"memory_size",
"version_number",
"is_continuous_control",
"action_output_shape",
]
def __init__(self, seed, brain, trainer_parameters):
"""
Initialized the policy.
:param seed: Random seed to use for TensorFlow.
:param brain: The corresponding Brain for this policy.
:param trainer_parameters: The trainer parameters.
"""
self.m_size = None
self.model = None
self.inference_dict = {}
self.update_dict = {}
self.sequence_length = 1
self.seed = seed
self.brain = brain
self.use_recurrent = trainer_parameters["use_recurrent"]
self.normalize = trainer_parameters.get("normalize", False)
self.use_continuous_act = brain.vector_action_space_type == "continuous"
self.model_path = trainer_parameters["model_path"]
self.keep_checkpoints = trainer_parameters.get("keep_checkpoints", 5)
self.graph = tf.Graph()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# For multi-GPU training, set allow_soft_placement to True to allow
# placing the operation into an alternative device automatically
# to prevent from exceptions if the device doesn't suppport the operation
# or the device does not exist
config.allow_soft_placement = True
self.sess = tf.Session(config=config, graph=self.graph)
self.saver = None
if self.use_recurrent:
self.m_size = trainer_parameters["memory_size"]
self.sequence_length = trainer_parameters["sequence_length"]
if self.m_size == 0:
raise UnityPolicyException(
"The memory size for brain {0} is 0 even "
"though the trainer uses recurrent.".format(brain.brain_name)
)
elif self.m_size % 4 != 0:
raise UnityPolicyException(
"The memory size for brain {0} is {1} "
"but it must be divisible by 4.".format(
brain.brain_name, self.m_size
)
)
def _initialize_graph(self):
with self.graph.as_default():
self.saver = tf.train.Saver(max_to_keep=self.keep_checkpoints)
init = tf.global_variables_initializer()
self.sess.run(init)
def _load_graph(self):
with self.graph.as_default():
self.saver = tf.train.Saver(max_to_keep=self.keep_checkpoints)
logger.info("Loading Model for brain {}".format(self.brain.brain_name))
ckpt = tf.train.get_checkpoint_state(self.model_path)
if ckpt is None:
logger.info(
"The model {0} could not be found. Make "
"sure you specified the right "
"--run-id".format(self.model_path)
)
self.saver.restore(self.sess, ckpt.model_checkpoint_path)
def evaluate(self, brain_info: BrainInfo) -> Dict[str, Any]:
"""
Evaluates policy for the agent experiences provided.
:param brain_info: BrainInfo input to network.
:return: Output from policy based on self.inference_dict.
"""
raise UnityPolicyException("The evaluate function was not implemented.")
def get_action(self, brain_info: BrainInfo) -> ActionInfo:
"""
Decides actions given observations information, and takes them in environment.
:param brain_info: A dictionary of brain names and BrainInfo from environment.
:return: an ActionInfo containing action, memories, values and an object
to be passed to add experiences
"""
if len(brain_info.agents) == 0:
return ActionInfo([], [], [], None, None)
run_out = self.evaluate(brain_info)
return ActionInfo(
action=run_out.get("action"),
memory=run_out.get("memory_out"),
text=None,
value=run_out.get("value"),
outputs=run_out,
)
def update(self, mini_batch, num_sequences):
"""
Performs update of the policy.
:param num_sequences: Number of experience trajectories in batch.
:param mini_batch: Batch of experiences.
:return: Results of update.
"""
raise UnityPolicyException("The update function was not implemented.")
def _execute_model(self, feed_dict, out_dict):
"""
Executes model.
:param feed_dict: Input dictionary mapping nodes to input data.
:param out_dict: Output dictionary mapping names to nodes.
:return: Dictionary mapping names to input data.
"""
network_out = self.sess.run(list(out_dict.values()), feed_dict=feed_dict)
run_out = dict(zip(list(out_dict.keys()), network_out))
return run_out
def fill_eval_dict(self, feed_dict, brain_info):
for i, _ in enumerate(brain_info.visual_observations):
feed_dict[self.model.visual_in[i]] = brain_info.visual_observations[i]
if self.use_vec_obs:
feed_dict[self.model.vector_in] = brain_info.vector_observations
if not self.use_continuous_act:
feed_dict[self.model.action_masks] = brain_info.action_masks
return feed_dict
def make_empty_memory(self, num_agents):
"""
Creates empty memory for use with RNNs
:param num_agents: Number of agents.
:return: Numpy array of zeros.
"""
return np.zeros((num_agents, self.m_size))
def get_current_step(self):
"""
Gets current model step.
:return: current model step.
"""
step = self.sess.run(self.model.global_step)
return step
def increment_step(self, n_steps):
"""
Increments model step.
"""
out_dict = {
"global_step": self.model.global_step,
"increment_step": self.model.increment_step,
}
feed_dict = {self.model.steps_to_increment: n_steps}
return self.sess.run(out_dict, feed_dict=feed_dict)["global_step"]
def get_inference_vars(self):
"""
:return:list of inference var names
"""
return list(self.inference_dict.keys())
def get_update_vars(self):
"""
:return:list of update var names
"""
return list(self.update_dict.keys())
def save_model(self, steps):
"""
Saves the model
:param steps: The number of steps the model was trained for
:return:
"""
with self.graph.as_default():
last_checkpoint = self.model_path + "/model-" + str(steps) + ".cptk"
self.saver.save(self.sess, last_checkpoint)
tf.train.write_graph(
self.graph, self.model_path, "raw_graph_def.pb", as_text=False
)
def export_model(self):
"""
Exports latest saved model to .nn format for Unity embedding.
"""
with self.graph.as_default():
target_nodes = ",".join(self._process_graph())
graph_def = self.graph.as_graph_def()
output_graph_def = graph_util.convert_variables_to_constants(
self.sess, graph_def, target_nodes.replace(" ", "").split(",")
)
frozen_graph_def_path = self.model_path + "/frozen_graph_def.pb"
with gfile.GFile(frozen_graph_def_path, "wb") as f:
f.write(output_graph_def.SerializeToString())
tf2bc.convert(frozen_graph_def_path, self.model_path + ".nn")
logger.info("Exported " + self.model_path + ".nn file")
def _process_graph(self):
"""
Gets the list of the output nodes present in the graph for inference
:return: list of node names
"""
all_nodes = [x.name for x in self.graph.as_graph_def().node]
nodes = [x for x in all_nodes if x in self.possible_output_nodes]
logger.info("List of nodes to export for brain :" + self.brain.brain_name)
for n in nodes:
logger.info("\t" + n)
return nodes
def update_normalization(self, vector_obs: np.ndarray) -> None:
"""
If this policy normalizes vector observations, this will update the norm values in the graph.
:param vector_obs: The vector observations to add to the running estimate of the distribution.
"""
if self.use_vec_obs and self.normalize:
self.sess.run(
self.model.update_normalization,
feed_dict={self.model.vector_in: vector_obs},
)
@property
def vis_obs_size(self):
return self.model.vis_obs_size
@property
def vec_obs_size(self):
return self.model.vec_obs_size
@property
def use_vis_obs(self):
return self.model.vis_obs_size > 0
@property
def use_vec_obs(self):
return self.model.vec_obs_size > 0