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actor_learner.py
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actor_learner.py
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import numpy as np
from multiprocessing import Process
import tensorflow as tf
import logging
from logger_utils import variable_summaries
import os
import json
class ActorLearner(Process):
def __init__(self, network_creator, environment_creator, explo_policy, args):
super(ActorLearner, self).__init__()
# Folder and debug settings
self.checkpoint_interval = args.checkpoint_interval
self.debugging_folder = args.debugging_folder
self.network_checkpoint_folder = os.path.join(self.debugging_folder, 'checkpoints/')
self.optimizer_checkpoint_folder = os.path.join(self.debugging_folder, 'optimizer_checkpoints/')
self.last_saving_step = 0
self.device = args.device
# Reinforcement learning settings
self.game = args.game
self.global_step = 0
self.max_global_steps = args.max_global_steps
self.max_local_steps = args.max_local_steps
self.num_actions = args.num_actions
self.explo_policy = explo_policy
self.gamma = args.gamma
self.initial_lr = args.initial_lr
self.lr_annealing_steps = args.lr_annealing_steps
self.emulator_counts = args.emulator_counts
self.emulators = np.asarray([environment_creator.create_environment(i)
for i in range(self.emulator_counts)])
self.max_global_steps = args.max_global_steps
self.gamma = args.gamma
self.network = network_creator()
with tf.name_scope('Optimizer'):
self.learning_rate = tf.placeholder(tf.float32, shape=[], name='lr')
# Optimizer
optimizer_variable_names = 'OptimizerVariables'
self.optimizer = tf.train.RMSPropOptimizer(self.learning_rate, decay=args.alpha, epsilon=args.e,
name=optimizer_variable_names)
grads_and_vars = self.optimizer.compute_gradients(self.network.loss)
self.flat_raw_gradients = tf.concat([tf.reshape(g, [-1]) for g, v in grads_and_vars], axis=0)
# This is not really an operation, but a list of gradient Tensors.
# When calling run() on it, the value of those Tensors
# (i.e., of the gradients) will be calculated
if args.clip_norm_type == 'ignore':
# Unclipped gradients
global_norm = tf.global_norm([g for g, v in grads_and_vars], name='global_norm')
elif args.clip_norm_type == 'global':
# Clip network grads by network norm
gradients_n_norm = tf.clip_by_global_norm(
[g for g, v in grads_and_vars], args.clip_norm)
global_norm = tf.identity(gradients_n_norm[1], name='global_norm')
grads_and_vars = list(zip(gradients_n_norm[0], [v for g, v in grads_and_vars]))
elif args.clip_norm_type == 'local':
# Clip layer grads by layer norm
gradients = [tf.clip_by_norm(
g, args.clip_norm) for g in grads_and_vars]
grads_and_vars = list(zip(gradients, [v for g, v in grads_and_vars]))
global_norm = tf.global_norm([g for g, v in grads_and_vars], name='global_norm')
else:
raise Exception('Norm type not recognized')
self.flat_clipped_gradients = tf.concat([tf.reshape(g, [-1]) for g, v in grads_and_vars], axis=0)
self.train_step = self.optimizer.apply_gradients(grads_and_vars)
config = tf.ConfigProto(allow_soft_placement = True)
if 'gpu' in self.device:
logging.debug('Dynamic gpu mem allocation')
config.gpu_options.allow_growth = True
self.session = tf.Session(config=config)
self.summary_writer = tf.summary.FileWriter(os.path.join(self.debugging_folder, 'tf'), self.session.graph)
self.network_saver = tf.train.Saver()
self.optimizer_variables = [var for var in tf.global_variables() if optimizer_variable_names in var.name]
self.optimizer_saver = tf.train.Saver(self.optimizer_variables, max_to_keep=1, name='OptimizerSaver')
# Summaries
variable_summaries(self.flat_raw_gradients, 'raw_gradients')
# variable_summaries(self.flat_clipped_gradients, 'clipped_gradients')
# tf.summary.scalar('global_norm', global_norm)
# tf.summary.scalar('loss/loss', self.network.loss)
# tf.summary.scalar('loss/critic_loss_mean', self.network.critic_loss_mean)
# tf.summary.scalar('loss/actor_objective_mean', self.network.actor_objective_mean)
# tf.summary.scalar('loss/actor_advantage_mean', self.network.actor_advantage_mean)
# tf.summary.scalar('loss/log_repetition_mean', self.network.log_repetition_mean)
# for i in range(len(grads_and_vars)):
# tf.summary.histogram('grads/grad-'+grads_and_vars[i][1].name[:-2], grads_and_vars[i][0])
def save_vars(self, force=False):
if force or self.global_step - self.last_saving_step >= self.checkpoint_interval:
self.last_saving_step = self.global_step
self.network_saver.save(self.session, self.network_checkpoint_folder, global_step=self.last_saving_step)
self.optimizer_saver.save(self.session, self.optimizer_checkpoint_folder, global_step=self.last_saving_step)
def rescale_reward(self, reward):
""" Clip immediate reward """
if reward > 1.0:
reward = 1.0
elif reward < -1.0:
reward = -1.0
return reward
def init_network(self):
import os
if not os.path.exists(self.network_checkpoint_folder):
os.makedirs(self.network_checkpoint_folder)
if not os.path.exists(self.optimizer_checkpoint_folder):
os.makedirs(self.optimizer_checkpoint_folder)
last_saving_step = self.network.init(self.network_checkpoint_folder, self.network_saver, self.session)
path = tf.train.latest_checkpoint(self.optimizer_checkpoint_folder)
if path is not None:
logging.info('Restoring optimizer variables from previous run')
self.optimizer_saver.restore(self.session, path)
return last_saving_step
def get_lr(self):
if self.global_step <= self.lr_annealing_steps:
return self.initial_lr - (self.global_step * self.initial_lr / self.lr_annealing_steps)
else:
return 0.0
def cleanup(self):
self.save_vars(True)
self.session.close()