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launcher.py
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launcher.py
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import os, sys
import argparse
import numpy as np
import theano
from ale_python_interface import ALEInterface
from train_agent import Q_Learning
def str2bool(v):
return v.lower() in ("yes", "true", "t", "1")
def process_args(args, defaults, description):
"""
Handle the command line.
args - list of command line arguments (not including executable name)
defaults - a name space with variables corresponding to each of
the required default command line values.
description - a string to display at the top of the help message.
"""
parser = argparse.ArgumentParser(description=description)
parser.add_argument('-r', '--rom', dest="rom", default=defaults.ROM,
help='ROM to run (default: %(default)s)')
parser.add_argument('-e', '--epochs', dest="epochs", type=int,
default=defaults.EPOCHS,
help='Number of training epochs (default: %(default)s)')
parser.add_argument('-s', '--steps-per-epoch', dest="steps_per_epoch",
type=int, default=defaults.STEPS_PER_EPOCH,
help='Number of steps per epoch (default: %(default)s)')
parser.add_argument('-t', '--test-length', dest="steps_per_test",
type=int, default=defaults.STEPS_PER_TEST,
help='Number of steps per test (default: %(default)s)')
parser.add_argument('--optimal-eps', dest='optimal_eps',
type=float, default=defaults.OPTIMAL_EPS,
help='Epsilon when playing optimally (default: %(default)s)')
parser.add_argument('--display-screen', dest="display_screen",
action='store_true', default=False,
help='Show the game screen.')
parser.add_argument('--testing', dest="testing",
action='store_true', default=False,
help='Signals running test.')
parser.add_argument('--experiment-prefix', dest="experiment_prefix",
default=None,
help='Experiment name prefix '
'(default is the name of the game)')
parser.add_argument('--frame-skip', dest="frame_skip",
default=defaults.FRAME_SKIP, type=int,
help='Every how many frames to process '
'(default: %(default)s)')
parser.add_argument('--update-rule', dest="update_rule",
type=str, default=defaults.UPDATE_RULE,
help=('adam|adadelta|rmsprop|sgd ' +
'(default: %(default)s)'))
parser.add_argument('--learning-rate', dest="learning_rate",
type=float, default=defaults.LEARNING_RATE,
help='Learning rate (default: %(default)s)')
parser.add_argument('--rms-decay', dest="rms_decay",
type=float, default=defaults.RMS_DECAY,
help='Decay rate for rms_prop (default: %(default)s)')
parser.add_argument('--rms-epsilon', dest="rms_epsilon",
type=float, default=defaults.RMS_EPSILON,
help='Denominator epsilson for rms_prop ' +
'(default: %(default)s)')
parser.add_argument('--clip-delta', dest="clip_delta", type=float,
default=defaults.CLIP_DELTA,
help=('Max absolute value for Q-update delta value. ' +
'(default: %(default)s)'))
parser.add_argument('--discount', type=float, default=defaults.DISCOUNT,
help='Discount rate')
parser.add_argument('--epsilon-start', dest="epsilon_start",
type=float, default=defaults.EPSILON_START,
help=('Starting value for epsilon. ' +
'(default: %(default)s)'))
parser.add_argument('--epsilon-min', dest="epsilon_min",
type=float, default=defaults.EPSILON_MIN,
help='Minimum epsilon. (default: %(default)s)')
parser.add_argument('--epsilon-decay', dest="epsilon_decay",
type=float, default=defaults.EPSILON_DECAY,
help=('Number of steps to minimum epsilon. ' +
'(default: %(default)s)'))
parser.add_argument('--phi-length', dest="phi_length",
type=int, default=defaults.PHI_LENGTH,
help=('Number of recent frames used to represent ' +
'state. (default: %(default)s)'))
parser.add_argument('--max-history', dest="replay_memory_size",
type=int, default=defaults.REPLAY_MEMORY_SIZE,
help=('Maximum number of steps stored in replay ' +
'memory. (default: %(default)s)'))
parser.add_argument('--batch-size', dest="batch_size",
type=int, default=defaults.BATCH_SIZE,
help='Batch size. (default: %(default)s)')
parser.add_argument('--freeze-interval', dest="freeze_interval",
type=int, default=defaults.FREEZE_INTERVAL,
help=('Interval between target freezes. ' +
'(default: %(default)s)'))
parser.add_argument('--update-frequency', dest="update_frequency",
type=int, default=defaults.UPDATE_FREQUENCY,
help=('Number of actions before each SGD update. '+
'(default: %(default)s)'))
parser.add_argument('--replay-start-size', dest="replay_start_size",
type=int, default=defaults.REPLAY_START_SIZE,
help=('Number of random steps before training. ' +
'(default: %(default)s)'))
parser.add_argument('--resize-method', dest="resize_method",
type=str, default=defaults.RESIZE_METHOD,
help=('crop|scale (default: %(default)s)'))
parser.add_argument('--crop-offset', dest="offset",
type=str, default=defaults.OFFSET,
help=('crop offset.'))
parser.add_argument('--nn-file', dest="nn_file", type=str, default=None,
help='Pickle file containing trained net.')
parser.add_argument('--cap-reward', dest="do_cap_reward",
type=str2bool, default=defaults.CAP_REWARD,
help=('true|false (default: %(default)s)'))
parser.add_argument('--death-ends-episode', dest="death_ends_episode",
type=str2bool, default=defaults.DEATH_ENDS_EPISODE,
help=('true|false (default: %(default)s)'))
parser.add_argument('--max-start-nullops', dest="max_start_nullops",
type=int, default=defaults.MAX_START_NULLOPS,
help=('Maximum number of null-ops at the start ' +
'of games. (default: %(default)s)'))
parser.add_argument('--folder-name', dest="folder_name",
type=str, default="",
help='Name of pkl files destination (within models/)')
parser.add_argument('--termination-reg', dest="termination_reg",
type=float, default=defaults.TERMINATION_REG,
help=('Regularization to decrease termination prob.'+
' (default: %(default)s)'))
parser.add_argument('--entropy-reg', dest="entropy_reg",
type=float, default=defaults.ENTROPY_REG,
help=('Regularization to increase policy entropy.'+
' (default: %(default)s)'))
parser.add_argument('--num-options', dest="num_options",
type=int, default=defaults.NUM_OPTIONS,
help=('Number of options to create.'+
' (default: %(default)s)'))
parser.add_argument('--actor-lr', dest="actor_lr",
type=float, default=defaults.ACTOR_LR,
help=('Actor network learning rate (default: %(default)s)'))
parser.add_argument('--double-q', dest='double_q',
type=str2bool, default=defaults.DOUBLE_Q,
help='Train using Double Q networks. (default: %(default)s)')
parser.add_argument('--mean-frame', dest='mean_frame',
type=str2bool, default=defaults.MEAN_FRAME,
help='Use pixel-wise mean consecutive frames as images. (default: %(default)s)')
parser.add_argument('--temp', dest='temp',
type=float, default=defaults.TEMP,
help='Action distribution softmax tempurature param. (default: %(default)s)')
parser.add_argument('--baseline', dest='baseline',
type=str2bool, default=defaults.BASELINE,
help='use baseline in actor gradient function. (default: %(default)s)')
parameters = parser.parse_args(args)
print parameters
if parameters.experiment_prefix is None:
name = os.path.splitext(os.path.basename(parameters.rom))[0]
parameters.experiment_prefix = name
return parameters
def load_params(model_path):
import pickle as pkl
mydir = "/".join(model_path.split("/")[:-1])
model_params = pkl.load(open(os.path.join(mydir, 'model_params.pkl'), 'rb'))
return model_params
def launch(args, defaults, description):
"""
Execute a complete training run.
"""
rec_screen = ""
if "--nn-file" in args:
temp_params = vars(load_params(args[args.index("--nn-file")+1]))
for p in temp_params:
try:
vars(defaults)[p.upper()] = temp_params[p]
except:
print "warning: parameter", p, "from param file doesn't exist."
#rec_screen = args[args.index("--nn-file")+1][:-len("last_model.pkl")]+"/frames"
parameters = process_args(args, defaults, description)
if parameters.rom.endswith('.bin'):
rom = parameters.rom
else:
rom = "%s.bin" % parameters.rom
parameters.rom_path = os.path.join(defaults.BASE_ROM_PATH, rom)
rng = np.random.RandomState(123456)
folder_name = None if parameters.folder_name == "" else parameters.folder_name
ale = ALEInterface()
ale.setInt('random_seed', rng.randint(1000))
ale.setBool('display_screen', parameters.display_screen)
ale.setString('record_screen_dir', rec_screen)
trainer = Q_Learning(model_params=parameters, ale_env=ale, folder_name=folder_name)
trainer.train()
if __name__ == '__main__':
pass