def __init__(self, args):
super(A3C_Learner, self).__init__()
self.actor_id = args.actor_id
self.game = args.game
self.gamma = args.gamma
self.entropy_regularisation_strength = args.entropy_regularisation_strength
self.batch_size = args.batch_size
self.checkpoint_interval = args.checkpoint_interval
self.file_init_weights = args.file_init_weights
self.name_save_file = args.name_save_file
self.local_step = 0
self.global_step = args.global_step
self.barrier = args.barrier
self.queue = args.queue
self.max_global_steps = args.max_global_steps
self.pipes = args.pipes
self.thread_step_counter = 1
self.nb_actions = args.nb_actions
self.epsilon = args.epsilon
self.num_actor_learners = args.num_actor_learners
self.env = atari_environment.AtariEnvironment(args.game,
visualize=args.visualize)
self.logger = logging_utils.getLogger(
__name__ + ":Process {}".format(self.actor_id))
def __init__(self, conf):
self.name = conf['name']
self.nb_actions = conf['nb_actions']
self.gamma = conf['gamma']
self.actor_id = None if 'actor_id' not in conf else conf['actor_id']
self.entropy_regularisation_strength = conf['entropy_regularisation_strength']
self.build_network()
self.create_assign_op_weights()
self.create_op_loss()
self._tf_session = tf.Session()
self._tf_session.run(tf.initialize_all_variables())
name_logger = __name__
if self.actor_id != None:
name_logger += ":Process {}".format(self.actor_id)
self.logger = logging_utils.getLogger(name_logger)
if self.actor_id == 0:
self.saver = tf.train.Saver(max_to_keep=10)
self.writer = tf.summary.FileWriter('./tf_logs/Process_{}'.format(self.actor_id), graph=self._tf_session.graph_def)
self._tf_summary_total_episode_reward = tf.placeholder(tf.float32, [])
self._tf_summary_len_episode = tf.placeholder(tf.float32, [])
tf.summary.scalar("total_episode_reward", self._tf_summary_total_episode_reward)
tf.summary.scalar("len_episode", self._tf_summary_len_episode)
self._tf_summary_op = tf.merge_all_summaries()
import sys
import os
import numpy as np
from threading import Thread, Lock
import tensorflow as tf
import logging_utils
import time
from emulator import get_num_actions
import importlib
from q_network import *
from policy_v_network import *
from value_based_actor_learner import *
from policy_based_actor_learner import *
import math
logger = logging_utils.getLogger('main')
def generate_epsilon():
""" Generate lower limit for decaying epsilon. """
epsilon = {'limits': [0.1, 0.01, 0.5], 'probs': [0.4, 0.3, 0.3]}
return np.random.choice(epsilon['limits'], p=epsilon['probs'])
def check_or_create_checkpoint_dir(checkpoint_dir):
""" Create checkpoint directory if it does not exist """
if not os.path.exists(checkpoint_dir):
try:
os.makedirs(checkpoint_dir)
except OSError:
pass
from skimage.transform import resize
from skimage.color import rgb2gray
import numpy as np
import atari_py
import logging_utils
logger = logging_utils.getLogger('emulator')
import matplotlib.pyplot as plt
IMG_SCALE = 255.0
IMG_SIZE_X = 84
IMG_SIZE_Y = 84
NR_IMAGES = 4
def get_num_actions(rom_path, rom_name):
#import os
#print os.path.abspath(atari_py.__file__)
game_path = atari_py.get_game_path(rom_name)
ale = atari_py.ALEInterface()
ale.loadROM(game_path)
return ale.getMinimalActionSet()
class Emulator:
def __init__(self, rom_path, rom_name, visualize, actor_id, rseed, single_life_episode = False):
self.ale = atari_py.ALEInterface()
self.ale.setInt("random_seed", rseed * (actor_id +1))
# For fuller control on explicit action repeat (>= ALE 0.5.0)
import numpy as np
from multiprocessing import Process
import logging_utils
import tensorflow as tf
import ctypes
import pyximport
pyximport.install()
from hogupdatemv import copy, apply_grads_mom_rmsprop, apply_grads_adam
import time
import utils
CHECKPOINT_INTERVAL = 500000
logger = logging_utils.getLogger('actor_learner')
def generate_final_epsilon():
""" Generate lower limit for decaying epsilon. """
epsilon = {'limits': [0.1, 0.01, 0.5], 'probs': [0.4, 0.3, 0.3]}
return np.random.choice(epsilon['limits'], p=epsilon['probs'])
class ActorLearner(Process):
def __init__(self, args):
super(ActorLearner, self).__init__()
self.summ_base_dir = args.summ_base_dir
self.local_step = 0
self.global_step = args.global_step
import argparse
from multiprocessing import Process, Value, Barrier, Queue, Pipe
import numpy as np
from A3C_Learner import A3C_Learner
import time
import logging_utils
import atari_environment
logger = logging_utils.getLogger(__name__)
def main(args):
logger.debug("CONFIGURATION : {}".format(args))
#Global shared counter alloated in the shared memory. i = signed int
args.global_step = Value('i', 0)
#Barrier used to synchronize the threads
args.barrier = Barrier(args.num_actor_learners)
#Thread safe queue used to communicate between the threads
args.queue = Queue()
#Number of actions available at each steps of the game
args.nb_actions = atari_environment.get_num_actions(args.game)
if args.visualize == 0:
args.visualize = False
else:
args.visualize = True
actor_learners = []
import numpy as np
from multiprocessing import Process
import logging_utils
import tensorflow as tf
import ctypes
import pyximport; pyximport.install()
from hogupdatemv import copy, apply_grads_mom_rmsprop, apply_grads_adam
import time
import utils
CHECKPOINT_INTERVAL = 500000
logger = logging_utils.getLogger('actor_learner')
def generate_final_epsilon():
""" Generate lower limit for decaying epsilon. """
epsilon = {'limits': [0.1, 0.01, 0.5], 'probs': [0.4, 0.3, 0.3]}
return np.random.choice(epsilon['limits'], p=epsilon['probs'])
class ActorLearner(Process):
def __init__(self, args):
super(ActorLearner, self).__init__()
self.summ_base_dir = args.summ_base_dir
self.local_step = 0
self.global_step = args.global_step
self.actor_id = args.actor_id
import sys
import os
import numpy as np
from threading import Thread, Lock
import tensorflow as tf
import logging_utils
import time
from emulator import get_num_actions
import importlib
from q_network import *
from policy_v_network import *
from value_based_actor_learner import *
from policy_based_actor_learner import *
import math
logger = logging_utils.getLogger('main')
def generate_epsilon():
""" Generate lower limit for decaying epsilon. """
epsilon = {'limits': [0.1, 0.01, 0.5], 'probs': [0.4, 0.3, 0.3]}
return np.random.choice(epsilon['limits'], p=epsilon['probs'])
def check_or_create_checkpoint_dir(checkpoint_dir):
""" Create checkpoint directory if it does not exist """
if not os.path.exists(checkpoint_dir):
try:
os.makedirs(checkpoint_dir)
except OSError:
pass
def restore_vars(saver, sess, game_name, actor_learner_type,
# encoding: utf-8
import abc
import os
from logging_utils import getLogger, SentinelBuilder
from ._compat import with_metaclass
logger = getLogger(__name__)
sentinel = SentinelBuilder(logger)
class Client(with_metaclass(abc.ABCMeta)):
def is_alive(self, pid):
try:
with logger.context(pid=pid), sentinel('Checking pid owner liveness'):
return self._is_alive(pid)
except:
return False
@abc.abstractmethod
def _is_alive(self, pid):
pass # pragma: no cover
def terminate(self, pid):
with logger.context(pid=pid), sentinel('Terminating pid owner'):
return self._terminate(pid)
@abc.abstractmethod
def _terminate(self, pid):
pass # pragma: no cover
