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。
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
 
 
 
 
 

446 行
22 KiB

# # Unity ML Agents
# ## ML-Agent Learning (PPO)
# Contains an implementation of PPO as described [here](https://arxiv.org/abs/1707.06347).
import logging
import os
import numpy as np
import tensorflow as tf
from unityagents import AllBrainInfo
from unitytrainers.buffer import Buffer
from unitytrainers.ppo.models import PPOModel
from unitytrainers.trainer import UnityTrainerException, Trainer
logger = logging.getLogger("unityagents")
class PPOTrainer(Trainer):
"""The PPOTrainer is an implementation of the PPO algorythm."""
def __init__(self, sess, env, brain_name, trainer_parameters, training, seed):
"""
Responsible for collecting experiences and training PPO model.
:param sess: Tensorflow session.
:param env: The UnityEnvironment.
:param trainer_parameters: The parameters for the trainer (dictionary).
:param training: Whether the trainer is set for training.
"""
self.param_keys = ['batch_size', 'beta', 'buffer_size', 'epsilon', 'gamma', 'hidden_units', 'lambd',
'learning_rate',
'max_steps', 'normalize', 'num_epoch', 'num_layers', 'time_horizon', 'sequence_length',
'summary_freq',
'use_recurrent', 'graph_scope', 'summary_path', 'memory_size']
for k in self.param_keys:
if k not in trainer_parameters:
raise UnityTrainerException("The hyperparameter {0} could not be found for the PPO trainer of "
"brain {1}.".format(k, brain_name))
super(PPOTrainer, self).__init__(sess, env, brain_name, trainer_parameters, training)
self.use_recurrent = trainer_parameters["use_recurrent"]
self.sequence_length = 1
self.m_size = None
if self.use_recurrent:
self.m_size = trainer_parameters["memory_size"]
self.sequence_length = trainer_parameters["sequence_length"]
if self.use_recurrent:
if self.m_size == 0:
raise UnityTrainerException("The memory size for brain {0} is 0 even though the trainer uses recurrent."
.format(brain_name))
elif self.m_size % 4 != 0:
raise UnityTrainerException("The memory size for brain {0} is {1} but it must be divisible by 4."
.format(brain_name, self.m_size))
self.variable_scope = trainer_parameters['graph_scope']
with tf.variable_scope(self.variable_scope):
tf.set_random_seed(seed)
self.model = PPOModel(env.brains[brain_name],
lr=float(trainer_parameters['learning_rate']),
h_size=int(trainer_parameters['hidden_units']),
epsilon=float(trainer_parameters['epsilon']),
beta=float(trainer_parameters['beta']),
max_step=float(trainer_parameters['max_steps']),
normalize=trainer_parameters['normalize'],
use_recurrent=trainer_parameters['use_recurrent'],
num_layers=int(trainer_parameters['num_layers']),
m_size=self.m_size)
stats = {'cumulative_reward': [], 'episode_length': [], 'value_estimate': [],
'entropy': [], 'value_loss': [], 'policy_loss': [], 'learning_rate': []}
self.stats = stats
self.training_buffer = Buffer()
self.cumulative_rewards = {}
self.episode_steps = {}
self.is_continuous = (env.brains[brain_name].vector_action_space_type == "continuous")
self.use_observations = (env.brains[brain_name].number_visual_observations > 0)
self.use_states = (env.brains[brain_name].vector_observation_space_size > 0)
self.summary_path = trainer_parameters['summary_path']
if not os.path.exists(self.summary_path):
os.makedirs(self.summary_path)
self.summary_writer = tf.summary.FileWriter(self.summary_path)
def __str__(self):
return '''Hypermarameters for the PPO Trainer of brain {0}: \n{1}'''.format(
self.brain_name, '\n'.join(['\t{0}:\t{1}'.format(x, self.trainer_parameters[x]) for x in self.param_keys]))
@property
def parameters(self):
"""
Returns the trainer parameters of the trainer.
"""
return self.trainer_parameters
@property
def graph_scope(self):
"""
Returns the graph scope of the trainer.
"""
return self.variable_scope
@property
def get_max_steps(self):
"""
Returns the maximum number of steps. Is used to know when the trainer should be stopped.
:return: The maximum number of steps of the trainer
"""
return float(self.trainer_parameters['max_steps'])
@property
def get_step(self):
"""
Returns the number of steps the trainer has performed
:return: the step count of the trainer
"""
return self.sess.run(self.model.global_step)
@property
def get_last_reward(self):
"""
Returns the last reward the trainer has had
:return: the new last reward
"""
return self.sess.run(self.model.last_reward)
def increment_step(self):
"""
Increment the step count of the trainer
"""
self.sess.run(self.model.increment_step)
def update_last_reward(self):
"""
Updates the last reward
"""
if len(self.stats['cumulative_reward']) > 0:
mean_reward = np.mean(self.stats['cumulative_reward'])
self.sess.run(self.model.update_reward, feed_dict={self.model.new_reward: mean_reward})
def running_average(self, data, steps, running_mean, running_variance):
"""
Computes new running mean and variances.
:param data: New piece of data.
:param steps: Total number of data so far.
:param running_mean: TF op corresponding to stored running mean.
:param running_variance: TF op corresponding to stored running variance.
:return: New mean and variance values.
"""
mean, var = self.sess.run([running_mean, running_variance])
current_x = np.mean(data, axis=0)
new_mean = mean + (current_x - mean) / (steps + 1)
new_variance = var + (current_x - new_mean) * (current_x - mean)
return new_mean, new_variance
def take_action(self, all_brain_info: AllBrainInfo):
"""
Decides actions given state/observation information, and takes them in environment.
:param all_brain_info: A dictionary of brain names and BrainInfo from environment.
:return: a tuple containing action, memories, values and an object
to be passed to add experiences
"""
steps = self.get_step
curr_brain_info = all_brain_info[self.brain_name]
if len(curr_brain_info.agents) == 0:
return [], [], [], None
feed_dict = {self.model.batch_size: len(curr_brain_info.vector_observations), self.model.sequence_length: 1}
run_list = [self.model.output, self.model.all_probs, self.model.value, self.model.entropy,
self.model.learning_rate]
if self.is_continuous:
run_list.append(self.model.epsilon)
elif self.use_recurrent:
feed_dict[self.model.prev_action] = np.reshape(curr_brain_info.previous_vector_actions, [-1])
if self.use_observations:
for i, _ in enumerate(curr_brain_info.visual_observations):
feed_dict[self.model.visual_in[i]] = curr_brain_info.visual_observations[i]
if self.use_states:
feed_dict[self.model.vector_in] = curr_brain_info.vector_observations
if self.use_recurrent:
if curr_brain_info.memories.shape[1] == 0:
curr_brain_info.memories = np.zeros((len(curr_brain_info.agents), self.m_size))
feed_dict[self.model.memory_in] = curr_brain_info.memories
run_list += [self.model.memory_out]
if (self.is_training and self.brain.vector_observation_space_type == "continuous" and
self.use_states and self.trainer_parameters['normalize']):
new_mean, new_variance = self.running_average(
curr_brain_info.vector_observations, steps, self.model.running_mean, self.model.running_variance)
feed_dict[self.model.new_mean] = new_mean
feed_dict[self.model.new_variance] = new_variance
run_list = run_list + [self.model.update_mean, self.model.update_variance]
values = self.sess.run(run_list, feed_dict=feed_dict)
run_out = dict(zip(run_list, values))
self.stats['value_estimate'].append(run_out[self.model.value].mean())
self.stats['entropy'].append(run_out[self.model.entropy].mean())
self.stats['learning_rate'].append(run_out[self.model.learning_rate])
if self.use_recurrent:
return (run_out[self.model.output],
run_out[self.model.memory_out],
[str(v) for v in run_out[self.model.value]],
run_out)
else:
return (run_out[self.model.output],
None,
[str(v) for v in run_out[self.model.value]],
run_out)
def add_experiences(self, curr_all_info: AllBrainInfo, next_all_info: AllBrainInfo, take_action_outputs):
"""
Adds experiences to each agent's experience history.
:param curr_all_info: Dictionary of all current brains and corresponding BrainInfo.
:param next_all_info: Dictionary of all current brains and corresponding BrainInfo.
:param take_action_outputs: The outputs of the take action method.
"""
curr_info = curr_all_info[self.brain_name]
next_info = next_all_info[self.brain_name]
for agent_id in curr_info.agents:
self.training_buffer[agent_id].last_brain_info = curr_info
self.training_buffer[agent_id].last_take_action_outputs = take_action_outputs
for agent_id in next_info.agents:
stored_info = self.training_buffer[agent_id].last_brain_info
stored_take_action_outputs = self.training_buffer[agent_id].last_take_action_outputs
if stored_info is None:
continue
else:
idx = stored_info.agents.index(agent_id)
next_idx = next_info.agents.index(agent_id)
if not stored_info.local_done[idx]:
if self.use_observations:
for i, _ in enumerate(stored_info.visual_observations):
self.training_buffer[agent_id]['observations%d' % i].append(stored_info.visual_observations[i][idx])
if self.use_states:
self.training_buffer[agent_id]['states'].append(stored_info.vector_observations[idx])
if self.use_recurrent:
if stored_info.memories.shape[1] == 0:
stored_info.memories = np.zeros((len(stored_info.agents), self.m_size))
self.training_buffer[agent_id]['memory'].append(stored_info.memories[idx])
if self.is_continuous:
epsi = stored_take_action_outputs[self.model.epsilon]
self.training_buffer[agent_id]['epsilons'].append(epsi[idx])
actions = stored_take_action_outputs[self.model.output]
a_dist = stored_take_action_outputs[self.model.all_probs]
value = stored_take_action_outputs[self.model.value]
self.training_buffer[agent_id]['actions'].append(actions[idx])
self.training_buffer[agent_id]['prev_action'].append(stored_info.previous_vector_actions[idx])
self.training_buffer[agent_id]['masks'].append(1.0)
self.training_buffer[agent_id]['rewards'].append(next_info.rewards[next_idx])
self.training_buffer[agent_id]['action_probs'].append(a_dist[idx])
self.training_buffer[agent_id]['value_estimates'].append(value[idx][0])
if agent_id not in self.cumulative_rewards:
self.cumulative_rewards[agent_id] = 0
self.cumulative_rewards[agent_id] += next_info.rewards[next_idx]
if agent_id not in self.episode_steps:
self.episode_steps[agent_id] = 0
self.episode_steps[agent_id] += 1
def process_experiences(self, all_info: AllBrainInfo):
"""
Checks agent histories for processing condition, and processes them as necessary.
Processing involves calculating value and advantage targets for model updating step.
:param all_info: Dictionary of all current brains and corresponding BrainInfo.
"""
info = all_info[self.brain_name]
for l in range(len(info.agents)):
agent_actions = self.training_buffer[info.agents[l]]['actions']
if ((info.local_done[l] or len(agent_actions) > self.trainer_parameters['time_horizon'])
and len(agent_actions) > 0):
if info.local_done[l] and not info.max_reached[l]:
value_next = 0.0
else:
feed_dict = {self.model.batch_size: len(info.vector_observations), self.model.sequence_length: 1}
if self.use_observations:
for i in range(len(info.visual_observations)):
feed_dict[self.model.visual_in[i]] = info.visual_observations[i]
if self.use_states:
feed_dict[self.model.vector_in] = info.vector_observations
if self.use_recurrent:
if info.memories.shape[1] == 0:
info.memories = np.zeros((len(info.vector_observations), self.m_size))
feed_dict[self.model.memory_in] = info.memories
if not self.is_continuous and self.use_recurrent:
feed_dict[self.model.prev_action] = np.reshape(info.previous_vector_actions, [-1])
value_next = self.sess.run(self.model.value, feed_dict)[l]
agent_id = info.agents[l]
self.training_buffer[agent_id]['advantages'].set(
get_gae(
rewards=self.training_buffer[agent_id]['rewards'].get_batch(),
value_estimates=self.training_buffer[agent_id]['value_estimates'].get_batch(),
value_next=value_next,
gamma=self.trainer_parameters['gamma'],
lambd=self.trainer_parameters['lambd'])
)
self.training_buffer[agent_id]['discounted_returns'].set(
self.training_buffer[agent_id]['advantages'].get_batch()
+ self.training_buffer[agent_id]['value_estimates'].get_batch())
self.training_buffer.append_update_buffer(agent_id,
batch_size=None, training_length=self.sequence_length)
self.training_buffer[agent_id].reset_agent()
if info.local_done[l]:
self.stats['cumulative_reward'].append(self.cumulative_rewards[agent_id])
self.stats['episode_length'].append(self.episode_steps[agent_id])
self.cumulative_rewards[agent_id] = 0
self.episode_steps[agent_id] = 0
def end_episode(self):
"""
A signal that the Episode has ended. The buffer must be reset.
Get only called when the academy resets.
"""
self.training_buffer.reset_all()
for agent_id in self.cumulative_rewards:
self.cumulative_rewards[agent_id] = 0
for agent_id in self.episode_steps:
self.episode_steps[agent_id] = 0
def is_ready_update(self):
"""
Returns whether or not the trainer has enough elements to run update model
:return: A boolean corresponding to whether or not update_model() can be run
"""
return len(self.training_buffer.update_buffer['actions']) > \
max(int(self.trainer_parameters['buffer_size'] / self.sequence_length), 1)
def update_model(self):
"""
Uses training_buffer to update model.
"""
num_epoch = self.trainer_parameters['num_epoch']
n_sequences = max(int(self.trainer_parameters['batch_size'] / self.sequence_length), 1)
total_v, total_p = 0, 0
advantages = self.training_buffer.update_buffer['advantages'].get_batch()
self.training_buffer.update_buffer['advantages'].set(
(advantages - advantages.mean()) / (advantages.std() + 1e-10))
for k in range(num_epoch):
self.training_buffer.update_buffer.shuffle()
for l in range(len(self.training_buffer.update_buffer['actions']) // n_sequences):
start = l * n_sequences
end = (l + 1) * n_sequences
_buffer = self.training_buffer.update_buffer
feed_dict = {self.model.batch_size: n_sequences,
self.model.sequence_length: self.sequence_length,
self.model.mask_input: np.array(_buffer['masks'][start:end]).reshape(
[-1]),
self.model.returns_holder: np.array(_buffer['discounted_returns'][start:end]).reshape(
[-1]),
self.model.old_value: np.array(_buffer['value_estimates'][start:end]).reshape([-1]),
self.model.advantage: np.array(_buffer['advantages'][start:end]).reshape([-1, 1]),
self.model.all_old_probs: np.array(
_buffer['action_probs'][start:end]).reshape([-1, self.brain.vector_action_space_size])}
if self.is_continuous:
feed_dict[self.model.epsilon] = np.array(
_buffer['epsilons'][start:end]).reshape([-1, self.brain.vector_action_space_size])
else:
feed_dict[self.model.action_holder] = np.array(
_buffer['actions'][start:end]).reshape([-1])
if self.use_recurrent:
feed_dict[self.model.prev_action] = np.array(
_buffer['prev_action'][start:end]).reshape([-1])
if self.use_states:
if self.brain.vector_observation_space_type == "continuous":
feed_dict[self.model.vector_in] = np.array(
_buffer['states'][start:end]).reshape(
[-1, self.brain.vector_observation_space_size * self.brain.num_stacked_vector_observations])
else:
feed_dict[self.model.vector_in] = np.array(
_buffer['states'][start:end]).reshape([-1, self.brain.num_stacked_vector_observations])
if self.use_observations:
for i, _ in enumerate(self.model.visual_in):
_obs = np.array(_buffer['observations%d' % i][start:end])
(_batch, _seq, _w, _h, _c) = _obs.shape
feed_dict[self.model.visual_in[i]] = _obs.reshape([-1, _w, _h, _c])
if self.use_recurrent:
feed_dict[self.model.memory_in] = np.array(_buffer['memory'][start:end])[:, 0, :]
v_loss, p_loss, _ = self.sess.run(
[self.model.value_loss, self.model.policy_loss,
self.model.update_batch], feed_dict=feed_dict)
total_v += v_loss
total_p += p_loss
self.stats['value_loss'].append(total_v)
self.stats['policy_loss'].append(total_p)
self.training_buffer.reset_update_buffer()
def write_summary(self, lesson_number):
"""
Saves training statistics to Tensorboard.
:param lesson_number: The lesson the trainer is at.
"""
if (self.get_step % self.trainer_parameters['summary_freq'] == 0 and self.get_step != 0 and
self.is_training and self.get_step <= self.get_max_steps):
steps = self.get_step
if len(self.stats['cumulative_reward']) > 0:
mean_reward = np.mean(self.stats['cumulative_reward'])
logger.info(" {}: Step: {}. Mean Reward: {:0.3f}. Std of Reward: {:0.3f}."
.format(self.brain_name, steps, mean_reward, np.std(self.stats['cumulative_reward'])))
summary = tf.Summary()
for key in self.stats:
if len(self.stats[key]) > 0:
stat_mean = float(np.mean(self.stats[key]))
summary.value.add(tag='Info/{}'.format(key), simple_value=stat_mean)
self.stats[key] = []
summary.value.add(tag='Info/Lesson', simple_value=lesson_number)
self.summary_writer.add_summary(summary, steps)
self.summary_writer.flush()
def discount_rewards(r, gamma=0.99, value_next=0.0):
"""
Computes discounted sum of future rewards for use in updating value estimate.
:param r: List of rewards.
:param gamma: Discount factor.
:param value_next: T+1 value estimate for returns calculation.
:return: discounted sum of future rewards as list.
"""
discounted_r = np.zeros_like(r)
running_add = value_next
for t in reversed(range(0, r.size)):
running_add = running_add * gamma + r[t]
discounted_r[t] = running_add
return discounted_r
def get_gae(rewards, value_estimates, value_next=0.0, gamma=0.99, lambd=0.95):
"""
Computes generalized advantage estimate for use in updating policy.
:param rewards: list of rewards for time-steps t to T.
:param value_next: Value estimate for time-step T+1.
:param value_estimates: list of value estimates for time-steps t to T.
:param gamma: Discount factor.
:param lambd: GAE weighing factor.
:return: list of advantage estimates for time-steps t to T.
"""
value_estimates = np.asarray(value_estimates.tolist() + [value_next])
delta_t = rewards + gamma * value_estimates[1:] - value_estimates[:-1]
advantage = discount_rewards(r=delta_t, gamma=gamma * lambd)
return advantage