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
172 行
9.0 KiB
172 行
9.0 KiB
import numpy as np
|
|
import tensorflow as tf
|
|
|
|
from ppo.history import *
|
|
|
|
|
|
class Trainer(object):
|
|
def __init__(self, ppo_model, sess, info, is_continuous, use_observations):
|
|
"""
|
|
Responsible for collecting experinces and training PPO model.
|
|
:param ppo_model: Tensorflow graph defining model.
|
|
:param sess: Tensorflow session.
|
|
:param info: Environment BrainInfo object.
|
|
:param is_continuous: Whether action-space is continuous.
|
|
:param use_observations: Whether agent takes image observations.
|
|
"""
|
|
self.model = ppo_model
|
|
self.sess = sess
|
|
stats = {'cumulative_reward': [], 'episode_length': [], 'value_estimate': [],
|
|
'entropy': [], 'value_loss': [], 'policy_loss': []}
|
|
self.stats = stats
|
|
|
|
self.training_buffer = vectorize_history(empty_local_history({}))
|
|
|
|
self.history_dict = empty_all_history(info)
|
|
|
|
self.is_continuous = is_continuous
|
|
self.use_observations = use_observations
|
|
|
|
def take_action(self, info, env, brain_name):
|
|
"""
|
|
Decides actions given state/observation information, and takes them in environment.
|
|
:param info: Current BrainInfo from environment.
|
|
:param env: Environment to take actions in.
|
|
:param brain_name: Name of brain we are learning model for.
|
|
:return: BrainInfo corresponding to new environment state.
|
|
"""
|
|
epsi = None
|
|
if self.is_continuous:
|
|
epsi = np.random.randn(len(info.states), env.brains[brain_name].action_space_size)
|
|
feed_dict = {self.model.state_in: info.states, self.model.batch_size: len(info.states),
|
|
self.model.epsilon: epsi}
|
|
elif self.use_observations:
|
|
feed_dict = {self.model.observation_in: np.vstack(info.observations),
|
|
self.model.batch_size: len(info.states)}
|
|
else:
|
|
feed_dict = {self.model.state_in: info.states, self.model.batch_size: len(info.states)}
|
|
actions, a_dist, value, ent = self.sess.run([self.model.output, self.model.probs,
|
|
self.model.value, self.model.entropy],
|
|
feed_dict=feed_dict)
|
|
self.stats['value_estimate'].append(value)
|
|
self.stats['entropy'].append(ent)
|
|
new_info = env.step(actions, value={brain_name: value})[brain_name]
|
|
self.add_experiences(info, new_info, epsi, actions, a_dist, value)
|
|
return new_info
|
|
|
|
def add_experiences(self, info, next_info, epsi, actions, a_dist, value):
|
|
"""
|
|
Adds experiences to each agent's experience history.
|
|
:param info: Current BrainInfo.
|
|
:param next_info: Next BrainInfo.
|
|
:param epsi: Epsilon value (for continuous control)
|
|
:param actions: Chosen actions.
|
|
:param a_dist: Action probabilities.
|
|
:param value: Value estimates.
|
|
"""
|
|
for (agent, history) in self.history_dict.items():
|
|
if agent in info.agents:
|
|
idx = info.agents.index(agent)
|
|
if not info.local_done[idx]:
|
|
if self.use_observations:
|
|
history['observations'].append(info.observations[idx])
|
|
else:
|
|
history['states'].append(info.states[idx])
|
|
history['actions'].append(actions[idx])
|
|
history['rewards'].append(next_info.rewards[idx])
|
|
history['action_probs'].append(a_dist[idx])
|
|
if self.is_continuous:
|
|
history['epsilons'].append(epsi[idx])
|
|
history['value_estimates'].append(value[idx][0])
|
|
history['cumulative_reward'] += next_info.rewards[idx]
|
|
history['episode_steps'] += 1
|
|
|
|
def process_experiences(self, info, time_horizon, gamma, lambd):
|
|
"""
|
|
Checks agent histories for processing condition, and processes them as necessary.
|
|
Processing involves calculating value and advantage targets for model updating step.
|
|
:param info: Current BrainInfo
|
|
:param time_horizon: Max steps for individual agent history before processing.
|
|
:param gamma: Discount factor.
|
|
:param lambd: GAE factor.
|
|
"""
|
|
for l in range(len(info.agents)):
|
|
if (info.local_done[l] or len(self.history_dict[info.agents[l]]['actions']) > time_horizon) and len(
|
|
self.history_dict[info.agents[l]]['actions']) > 0:
|
|
if info.local_done[l]:
|
|
value_next = 0.0
|
|
else:
|
|
if self.use_observations:
|
|
feed_dict = {self.model.observation_in: np.vstack(info.observations),
|
|
self.model.batch_size: len(info.states)}
|
|
else:
|
|
feed_dict = {self.model.state_in: info.states,
|
|
self.model.batch_size: len(info.states)}
|
|
value_next = self.sess.run(self.model.value, feed_dict)[l]
|
|
history = vectorize_history(self.history_dict[info.agents[l]])
|
|
history['advantages'] = get_gae(rewards=history['rewards'],
|
|
value_estimates=history['value_estimates'],
|
|
value_next=value_next, gamma=gamma, lambd=lambd)
|
|
history['discounted_returns'] = history['advantages'] + history['value_estimates']
|
|
if len(self.training_buffer['actions']) > 0:
|
|
append_history(global_buffer=self.training_buffer, local_buffer=history)
|
|
else:
|
|
set_history(global_buffer=self.training_buffer, local_buffer=history)
|
|
self.history_dict[info.agents[l]] = empty_local_history(self.history_dict[info.agents[l]])
|
|
if info.local_done[l]:
|
|
self.stats['cumulative_reward'].append(history['cumulative_reward'])
|
|
self.stats['episode_length'].append(history['episode_steps'])
|
|
history['cumulative_reward'] = 0
|
|
history['episode_steps'] = 0
|
|
|
|
def update_model(self, batch_size, num_epoch):
|
|
"""
|
|
Uses training_buffer to update model.
|
|
:param batch_size: Size of each mini-batch update.
|
|
:param num_epoch: How many passes through data to update model for.
|
|
"""
|
|
total_v, total_p = 0, 0
|
|
advantages = self.training_buffer['advantages']
|
|
self.training_buffer['advantages'] = (advantages - advantages.mean()) / advantages.std()
|
|
for k in range(num_epoch):
|
|
training_buffer = shuffle_buffer(self.training_buffer)
|
|
for l in range(len(training_buffer['actions']) // batch_size):
|
|
start = l * batch_size
|
|
end = (l + 1) * batch_size
|
|
feed_dict = {self.model.returns_holder: training_buffer['discounted_returns'][start:end],
|
|
self.model.advantage: np.vstack(training_buffer['advantages'][start:end]),
|
|
self.model.old_probs: np.vstack(training_buffer['action_probs'][start:end])}
|
|
if self.is_continuous:
|
|
feed_dict[self.model.epsilon] = np.vstack(training_buffer['epsilons'][start:end])
|
|
feed_dict[self.model.state_in] = np.vstack(training_buffer['states'][start:end])
|
|
else:
|
|
feed_dict[self.model.action_holder] = np.hstack(training_buffer['actions'][start:end])
|
|
if self.use_observations:
|
|
feed_dict[self.model.observation_in] = np.vstack(training_buffer['observations'][start:end])
|
|
else:
|
|
feed_dict[self.model.state_in] = np.vstack(training_buffer['states'][start:end])
|
|
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 = vectorize_history(empty_local_history({}))
|
|
for key in self.history_dict:
|
|
self.history_dict[key] = empty_local_history(self.history_dict[key])
|
|
|
|
def write_summary(self, summary_writer, steps):
|
|
"""
|
|
Saves training statistics to Tensorboard.
|
|
:param summary_writer: writer associated with Tensorflow session.
|
|
:param steps: Number of environment steps in training process.
|
|
"""
|
|
print("Mean Reward: {0}".format(np.mean(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_writer.add_summary(summary, steps)
|
|
summary_writer.flush()
|