def launch(args, defaults, description):
"""
Execute a complete training run.
"""
logging.basicConfig(level=logging.INFO)
parameters = process_args(args, defaults, description)
if parameters.rom.endswith('.bin'):
rom = parameters.rom
else:
rom = "%s.bin" % parameters.rom
full_rom_path = os.path.join(defaults.BASE_ROM_PATH, rom)
if parameters.deterministic:
rng = np.random.RandomState(123456)
else:
rng = np.random.RandomState()
ale = ale_python_interface.ALEInterface()
ale.setInt('random_seed', rng.randint(1000))
# FOR VISUALIZATION
USE_SDL = False
if parameters.display_screen:
if USE_SDL:
import sys
if sys.platform == 'darwin':
import pygame
pygame.init()
ale.setBool('sound', False) # Sound doesn't work on OSX
ale.setBool('display_screen', parameters.display_screen)
ale.setFloat('repeat_action_probability',
parameters.repeat_action_probability)
ale.loadROM(full_rom_path)
num_actions = len(ale.getMinimalActionSet())
agent = None
if parameters.use_episodic_control:
if parameters.qec_table is None:
qec_table = EC_functions.QECTable(
parameters.knn, parameters.state_dimension,
parameters.projection_type,
defaults.RESIZED_WIDTH * defaults.RESIZED_HEIGHT,
parameters.buffer_size, num_actions, rng)
else:
handle = open(parameters.qec_table, 'r')
qec_table = cPickle.load(handle)
#If this doesnt work load using the function below
# def try_to_load_as_pickled_object_or_None(filepath):
# """
# This is a defensive way to write pickle.load, allowing for very large files on all platforms
# """
# max_bytes = 2 ** 31 - 1
# try:
# input_size = os.path.getsize(filepath)
# bytes_in = bytearray(0)
# with open(filepath, 'rb') as f_in:
# for _ in range(0, input_size, max_bytes):
# bytes_in += f_in.read(max_bytes)
# obj = cPickle.loads(bytes_in)
# except:
# return None
# return obj
# qec_table = try_to_load_as_pickled_object_or_None(handle)
agent = IBL_agent.EpisodicControl(qec_table, parameters.ec_discount,
num_actions,
parameters.epsilon_start,
parameters.epsilon_min,
parameters.epsilon_decay,
parameters.experiment_prefix, rng)
experiment = ale_experiment.ALEExperiment(
ale, agent, defaults.RESIZED_WIDTH, defaults.RESIZED_HEIGHT,
parameters.resize_method, parameters.epochs,
parameters.steps_per_epoch, parameters.steps_per_test,
parameters.frame_skip, parameters.death_ends_episode,
parameters.max_start_nullops, rng)
experiment.run()
def launch(args, defaults, description):
"""
Execute a complete training run.
"""
logging.basicConfig(level=logging.INFO)
parameters = process_args(args, defaults, description)
if parameters.rom.endswith('.bin'):
rom = parameters.rom
else:
rom = "%s.bin" % parameters.rom
full_rom_path = os.path.join(defaults.BASE_ROM_PATH, rom)
if parameters.deterministic:
rng = np.random.RandomState(123456)
else:
rng = np.random.RandomState()
ale = ale_python_interface.ALEInterface()
ale.setInt('random_seed', rng.randint(1000))
if parameters.display_screen:
import sys
if sys.platform == 'darwin':
import pygame
pygame.init()
ale.setBool('sound', False) # Sound doesn't work on OSX
ale.setBool('display_screen', parameters.display_screen)
ale.setFloat('repeat_action_probability',
parameters.repeat_action_probability)
ale.loadROM(full_rom_path)
num_actions = len(ale.getMinimalActionSet())
agent = None
if parameters.use_episodic_control:
if parameters.qec_table is None:
qec_table = EC_functions.QECTable(
parameters.knn, parameters.state_dimension,
parameters.projection_type,
defaults.RESIZED_WIDTH * defaults.RESIZED_HEIGHT,
parameters.buffer_size, num_actions, rng)
else:
handle = open(parameters.qec_table, 'r')
qec_table = pickle.load(handle)
agent = EC_agent.EpisodicControl(qec_table, parameters.ec_discount,
num_actions, parameters.epsilon_start,
parameters.epsilon_min,
parameters.epsilon_decay,
parameters.experiment_prefix, rng)
experiment = ale_experiment.ALEExperiment(
ale, agent, defaults.RESIZED_WIDTH, defaults.RESIZED_HEIGHT,
parameters.resize_method, parameters.epochs,
parameters.steps_per_epoch, parameters.steps_per_test,
parameters.frame_skip, parameters.death_ends_episode,
parameters.max_start_nullops, rng)
experiment.run()
def launch(args, defaults, description):
"""
Execute a complete training run.
"""
logging.basicConfig(level=logging.INFO)
parameters = process_args(args, defaults, description)
if parameters.rom.endswith('.bin'):
rom = parameters.rom
else:
rom = "%s.bin" % parameters.rom
full_rom_path = os.path.join(defaults.BASE_ROM_PATH, rom)
if parameters.deterministic:
rng = np.random.RandomState(123456)
else:
rng = np.random.RandomState()
if parameters.cudnn_deterministic:
theano.config.dnn.conv.algo_bwd = 'deterministic'
ale = ale_python_interface.ALEInterface()
ale.setInt('random_seed', rng.randint(1000))
if parameters.display_screen:
import sys
if sys.platform == 'darwin':
import pygame
pygame.init()
ale.setBool('sound', False) # Sound doesn't work on OSX
ale.setBool('display_screen', parameters.display_screen)
ale.setFloat('repeat_action_probability',
parameters.repeat_action_probability)
ale.loadROM(full_rom_path)
num_actions = len(ale.getMinimalActionSet())
agent = None
if parameters.method == 'ec_dqn':
if parameters.nn_file is None:
network = q_network.DeepQLearner(defaults.RESIZED_WIDTH,
defaults.RESIZED_HEIGHT,
num_actions,
parameters.phi_length,
parameters.discount,
parameters.learning_rate,
parameters.rms_decay,
parameters.rms_epsilon,
parameters.momentum,
parameters.clip_delta,
parameters.freeze_interval,
parameters.batch_size,
parameters.network_type,
parameters.update_rule,
parameters.batch_accumulator,
rng, use_ec=True, double=parameters.double_dqn)
else:
handle = open(parameters.nn_file, 'r')
network = cPickle.load(handle)
if parameters.qec_table is None:
qec_table = EC_functions.QECTable(parameters.knn,
parameters.state_dimension,
parameters.projection_type,
defaults.RESIZED_WIDTH*defaults.RESIZED_HEIGHT,
parameters.buffer_size,
num_actions,
rng,
parameters.rebuild_knn_frequency)
else:
handle = open(parameters.qec_table, 'r')
qec_table = cPickle.load(handle)
agent = ale_agents.EC_DQN(network,
qec_table,
parameters.epsilon_start,
parameters.epsilon_min,
parameters.epsilon_decay,
parameters.replay_memory_size,
parameters.experiment_prefix,
parameters.replay_start_size,
parameters.update_frequency,
parameters.ec_discount,
num_actions,
parameters.ec_testing,
rng)
if parameters.method == 'dqn_episodic_memory1':
if parameters.nn_file is None:
network = q_network.DeepQLearner(defaults.RESIZED_WIDTH,
defaults.RESIZED_HEIGHT,
num_actions,
parameters.phi_length,
parameters.discount,
parameters.learning_rate,
parameters.rms_decay,
parameters.rms_epsilon,
parameters.momentum,
parameters.clip_delta,
parameters.freeze_interval,
parameters.batch_size,
parameters.network_type,
parameters.update_rule,
parameters.batch_accumulator,
rng, use_episodic_mem=True, double=parameters.double_dqn)
else:
handle = open(parameters.nn_file, 'r')
network = cPickle.load(handle)
if parameters.qec_table is None:
qec_table = EC_functions.QECTable(parameters.knn,
parameters.state_dimension,
parameters.projection_type,
defaults.RESIZED_WIDTH*defaults.RESIZED_HEIGHT,
parameters.buffer_size,
num_actions,
rng,
parameters.rebuild_knn_frequency)
else:
handle = open(parameters.qec_table, 'r')
qec_table = cPickle.load(handle)
agent = ale_agents.NeuralNetworkEpisodicMemory1(network,
qec_table,
parameters.epsilon_start,
parameters.epsilon_min,
parameters.epsilon_decay,
parameters.replay_memory_size,
parameters.experiment_prefix,
parameters.replay_start_size,
parameters.update_frequency,
parameters.ec_discount,
num_actions,
parameters.ec_testing,
rng)
if parameters.method == 'dqn_episodic_memory2':
if parameters.nn_file is None:
network = q_network.DeepQLearner(defaults.RESIZED_WIDTH,
defaults.RESIZED_HEIGHT,
num_actions,
parameters.phi_length,
parameters.discount,
parameters.learning_rate,
parameters.rms_decay,
parameters.rms_epsilon,
parameters.momentum,
parameters.clip_delta,
parameters.freeze_interval,
parameters.batch_size,
parameters.network_type,
parameters.update_rule,
parameters.batch_accumulator,
rng, use_episodic_mem=True, double=parameters.double_dqn)
else:
handle = open(parameters.nn_file, 'r')
network = cPickle.load(handle)
if parameters.qec_table is None:
qec_table = EC_functions.QECTable(parameters.knn,
parameters.state_dimension,
parameters.projection_type,
defaults.RESIZED_WIDTH*defaults.RESIZED_HEIGHT,
parameters.buffer_size,
num_actions,
rng,
parameters.rebuild_knn_frequency)
else:
handle = open(parameters.qec_table, 'r')
qec_table = cPickle.load(handle)
if parameters.method == 'dqn_episodic_memory3':
if parameters.nn_file is None:
network = q_network.DeepQLearner(defaults.RESIZED_WIDTH,
defaults.RESIZED_HEIGHT,
num_actions,
parameters.phi_length,
parameters.discount,
parameters.learning_rate,
parameters.rms_decay,
parameters.rms_epsilon,
parameters.momentum,
parameters.clip_delta,
parameters.freeze_interval,
parameters.batch_size,
parameters.network_type,
parameters.update_rule,
parameters.batch_accumulator,
rng, use_episodic_mem=True, double=parameters.double_dqn)
else:
handle = open(parameters.nn_file, 'r')
network = cPickle.load(handle)
if parameters.qec_table is None:
qec_table = EC_functions.LshHash(parameters.state_dimension,
defaults.RESIZED_WIDTH*defaults.RESIZED_HEIGHT,
parameters.buffer_size,
rng)
else:
handle = open(parameters.qec_table, 'r')
qec_table = cPickle.load(handle)
agent = ale_agents.NeuralNetworkEpisodicMemory3(network,
qec_table,
parameters.epsilon_start,
parameters.epsilon_min,
parameters.epsilon_decay,
parameters.replay_memory_size,
parameters.experiment_prefix,
parameters.replay_start_size,
parameters.update_frequency,
parameters.ec_discount,
num_actions,
parameters.ec_testing,
rng)
if parameters.method == 'dqn':
if parameters.nn_file is None:
network = q_network.DeepQLearner(defaults.RESIZED_WIDTH,
defaults.RESIZED_HEIGHT,
num_actions,
parameters.phi_length,
parameters.discount,
parameters.learning_rate,
parameters.rms_decay,
parameters.rms_epsilon,
parameters.momentum,
parameters.clip_delta,
parameters.freeze_interval,
parameters.batch_size,
parameters.network_type,
parameters.update_rule,
parameters.batch_accumulator,
rng, double=parameters.double_dqn)
else:
handle = open(parameters.nn_file, 'r')
network = cPickle.load(handle)
agent = ale_agents.NeuralAgent(network,
parameters.epsilon_start,
parameters.epsilon_min,
parameters.epsilon_decay,
parameters.replay_memory_size,
parameters.experiment_prefix,
parameters.replay_start_size,
parameters.update_frequency,
rng)
if parameters.method == 'episodic_control':
if parameters.qec_table is None:
qec_table = EC_functions.QECTable(parameters.knn,
parameters.state_dimension,
parameters.projection_type,
defaults.RESIZED_WIDTH*defaults.RESIZED_HEIGHT,
parameters.buffer_size,
num_actions,
rng,
parameters.rebuild_knn_frequency)
else:
handle = open(parameters.qec_table, 'r')
qec_table = cPickle.load(handle)
agent = ale_agents.EpisodicControl(qec_table,
parameters.ec_discount,
num_actions,
parameters.epsilon_start,
parameters.epsilon_min,
parameters.epsilon_decay,
parameters.experiment_prefix,
parameters.ec_testing,
rng)
experiment = ale_experiment.ALEExperiment(ale, agent,
defaults.RESIZED_WIDTH,
defaults.RESIZED_HEIGHT,
parameters.resize_method,
parameters.epochs,
parameters.steps_per_epoch,
parameters.steps_per_test,
parameters.frame_skip,
parameters.death_ends_episode,
parameters.max_start_nullops,
rng)
experiment.run()