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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# # Unity ML-Agents Toolkit
# ## ML-Agent Learning
"""Launches trainers for each External Brains in a Unity Environment."""
import os
import json
import logging
from typing import Dict, List, Optional
import numpy as np
import tensorflow as tf
from time import time
from mlagents.envs.env_manager import EnvironmentStep
from mlagents.envs.env_manager import EnvManager
from mlagents.envs.exception import (
UnityEnvironmentException,
UnityCommunicationException,
)
from mlagents.envs.sampler_class import SamplerManager
from mlagents.envs.timers import hierarchical_timer, get_timer_tree, timed
from mlagents.trainers.trainer import Trainer, TrainerMetrics
from mlagents.trainers.meta_curriculum import MetaCurriculum
class TrainerController(object):
def __init__(
self,
trainers: Dict[str, Trainer],
model_path: str,
summaries_dir: str,
run_id: str,
save_freq: int,
meta_curriculum: Optional[MetaCurriculum],
train: bool,
training_seed: int,
fast_simulation: bool,
sampler_manager: SamplerManager,
resampling_interval: Optional[int],
):
"""
:param trainers: Trainers for each brain to train.
:param model_path: Path to save the model.
:param summaries_dir: Folder to save training summaries.
:param run_id: The sub-directory name for model and summary statistics
:param save_freq: Frequency at which to save model
:param meta_curriculum: MetaCurriculum object which stores information about all curricula.
:param train: Whether to train model, or only run inference.
:param training_seed: Seed to use for Numpy and Tensorflow random number generation.
:param sampler_manager: SamplerManager object handles samplers for resampling the reset parameters.
:param resampling_interval: Specifies number of simulation steps after which reset parameters are resampled.
"""
self.trainers = trainers
self.model_path = model_path
self.summaries_dir = summaries_dir
self.logger = logging.getLogger("mlagents.envs")
self.run_id = run_id
self.save_freq = save_freq
self.train_model = train
self.trainer_metrics: Dict[str, TrainerMetrics] = {}
self.meta_curriculum = meta_curriculum
self.training_start_time = time()
self.fast_simulation = fast_simulation
self.sampler_manager = sampler_manager
self.resampling_interval = resampling_interval
np.random.seed(training_seed)
tf.set_random_seed(training_seed)
def _get_measure_vals(self):
brain_names_to_measure_vals = {}
if self.meta_curriculum:
for (
brain_name,
curriculum,
) in self.meta_curriculum.brains_to_curriculums.items():
if curriculum.measure == "progress":
measure_val = (
self.trainers[brain_name].get_step
/ self.trainers[brain_name].get_max_steps
)
brain_names_to_measure_vals[brain_name] = measure_val
elif curriculum.measure == "reward":
measure_val = np.mean(self.trainers[brain_name].reward_buffer)
brain_names_to_measure_vals[brain_name] = measure_val
else:
for brain_name, trainer in self.trainers.items():
measure_val = np.mean(trainer.reward_buffer)
brain_names_to_measure_vals[brain_name] = measure_val
return brain_names_to_measure_vals
def _save_model(self):
"""
Saves current model to checkpoint folder.
"""
for brain_name in self.trainers.keys():
self.trainers[brain_name].save_model()
self.logger.info("Saved Model")
def _save_model_when_interrupted(self):
self.logger.info(
"Learning was interrupted. Please wait while the graph is generated."
)
self._save_model()
def _write_training_metrics(self):
"""
Write all CSV metrics
:return:
"""
for brain_name in self.trainers.keys():
if brain_name in self.trainer_metrics:
self.trainers[brain_name].write_training_metrics()
def _write_timing_tree(self) -> None:
timing_path = f"{self.summaries_dir}/{self.run_id}_timers.json"
try:
with open(timing_path, "w") as f:
json.dump(get_timer_tree(), f, indent=2)
except FileNotFoundError:
self.logger.warning(
f"Unable to save to {timing_path}. Make sure the directory exists"
)
def _export_graph(self):
"""
Exports latest saved models to .nn format for Unity embedding.
"""
for brain_name in self.trainers.keys():
self.trainers[brain_name].export_model()
@staticmethod
def _create_model_path(model_path):
try:
if not os.path.exists(model_path):
os.makedirs(model_path)
except Exception:
raise UnityEnvironmentException(
"The folder {} containing the "
"generated model could not be "
"accessed. Please make sure the "
"permissions are set correctly.".format(model_path)
)
def _reset_env(self, env: EnvManager) -> List[EnvironmentStep]:
"""Resets the environment.
Returns:
A Data structure corresponding to the initial reset state of the
environment.
"""
sampled_reset_param = self.sampler_manager.sample_all()
new_meta_curriculum_config = (
self.meta_curriculum.get_config() if self.meta_curriculum else {}
)
sampled_reset_param.update(new_meta_curriculum_config)
return env.reset(train_mode=self.fast_simulation, config=sampled_reset_param)
def _should_save_model(self, global_step: int) -> bool:
return (
global_step % self.save_freq == 0 and global_step != 0 and self.train_model
)
def _not_done_training(self) -> bool:
return (
any([t.get_step <= t.get_max_steps for k, t in self.trainers.items()])
or not self.train_model
)
def write_to_tensorboard(self, global_step: int) -> None:
for brain_name, trainer in self.trainers.items():
# Write training statistics to Tensorboard.
delta_train_start = time() - self.training_start_time
if self.meta_curriculum is not None:
trainer.write_summary(
global_step,
delta_train_start,
lesson_num=self.meta_curriculum.brains_to_curriculums[
brain_name
].lesson_num,
)
else:
trainer.write_summary(global_step, delta_train_start)
def start_learning(self, env_manager: EnvManager) -> None:
self._create_model_path(self.model_path)
tf.reset_default_graph()
for _, t in self.trainers.items():
self.logger.info(t)
global_step = 0
if self.train_model:
for brain_name, trainer in self.trainers.items():
trainer.write_tensorboard_text("Hyperparameters", trainer.parameters)
try:
for brain_name, trainer in self.trainers.items():
env_manager.set_policy(brain_name, trainer.policy)
self._reset_env(env_manager)
while self._not_done_training():
n_steps = self.advance(env_manager)
for i in range(n_steps):
global_step += 1
self.reset_env_if_ready(env_manager, global_step)
if self._should_save_model(global_step):
# Save Tensorflow model
self._save_model()
self.write_to_tensorboard(global_step)
# Final save Tensorflow model
if global_step != 0 and self.train_model:
self._save_model()
except (KeyboardInterrupt, UnityCommunicationException):
if self.train_model:
self._save_model_when_interrupted()
pass
if self.train_model:
self._write_training_metrics()
self._export_graph()
self._write_timing_tree()
env_manager.close()
def end_trainer_episodes(
self, env: EnvManager, lessons_incremented: Dict[str, bool]
) -> None:
self._reset_env(env)
# Reward buffers reset takes place only for curriculum learning
# else no reset.
for brain_name, trainer in self.trainers.items():
trainer.end_episode()
for brain_name, changed in lessons_incremented.items():
if changed:
self.trainers[brain_name].reward_buffer.clear()
def reset_env_if_ready(self, env: EnvManager, steps: int) -> None:
if self.meta_curriculum:
# Get the sizes of the reward buffers.
reward_buff_sizes = {
k: len(t.reward_buffer) for (k, t) in self.trainers.items()
}
# Attempt to increment the lessons of the brains who
# were ready.
lessons_incremented = self.meta_curriculum.increment_lessons(
self._get_measure_vals(), reward_buff_sizes=reward_buff_sizes
)
else:
lessons_incremented = {}
# If any lessons were incremented or the environment is
# ready to be reset
meta_curriculum_reset = any(lessons_incremented.values())
# Check if we are performing generalization training and we have finished the
# specified number of steps for the lesson
generalization_reset = (
not self.sampler_manager.is_empty()
and (steps != 0)
and (self.resampling_interval)
and (steps % self.resampling_interval == 0)
)
if meta_curriculum_reset or generalization_reset:
self.end_trainer_episodes(env, lessons_incremented)
@timed
def advance(self, env: EnvManager) -> int:
with hierarchical_timer("env_step"):
time_start_step = time()
new_step_infos = env.step()
delta_time_step = time() - time_start_step
for step_info in new_step_infos:
for brain_name, trainer in self.trainers.items():
if brain_name in self.trainer_metrics:
self.trainer_metrics[brain_name].add_delta_step(delta_time_step)
trainer.add_experiences(
step_info.previous_all_brain_info,
step_info.current_all_brain_info,
step_info.brain_name_to_action_info[brain_name].outputs,
)
trainer.process_experiences(
step_info.previous_all_brain_info, step_info.current_all_brain_info
)
for brain_name, trainer in self.trainers.items():
if brain_name in self.trainer_metrics:
self.trainer_metrics[brain_name].add_delta_step(delta_time_step)
if self.train_model and trainer.get_step <= trainer.get_max_steps:
trainer.increment_step(len(new_step_infos))
if trainer.is_ready_update():
# Perform gradient descent with experience buffer
with hierarchical_timer("update_policy"):
trainer.update_policy()
env.set_policy(brain_name, trainer.policy)
return len(new_step_infos)