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train_dqn.py
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train_dqn.py
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from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
from __future__ import absolute_import
from builtins import * # NOQA
from future import standard_library
standard_library.install_aliases() # NOQA
import argparse
import os
from chainer import links as L
from chainer import optimizers
import numpy as np
import chainerrl
from chainerrl.action_value import DiscreteActionValue
from chainerrl import agents
from chainerrl import experiments
from chainerrl import explorers
from chainerrl import links
from chainerrl import misc
from chainerrl import replay_buffer
from chainerrl.wrappers import atari_wrappers
import json
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--env', type=str, default='BreakoutNoFrameskip-v4',
help='OpenAI Atari domain to perform algorithm on.')
parser.add_argument('--outdir', type=str, default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--seed', type=int, default=0,
help='Random seed [0, 2 ** 31)')
parser.add_argument('--gpu', type=int, default=0,
help='GPU to use, set to -1 if no GPU.')
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--logging-level', type=int, default=20,
help='Logging level. 10:DEBUG, 20:INFO etc.')
parser.add_argument('--render', action='store_true', default=False,
help='Render env states in a GUI window.')
parser.add_argument('--monitor', action='store_true', default=False,
help='Monitor env. Videos and additional information'
' are saved as output files.')
parser.add_argument('--steps', type=int, default=5 * 10 ** 7,
help='Total number of timesteps to train the agent.')
parser.add_argument('--replay-start-size', type=int, default=5 * 10 ** 4,
help='Minimum replay buffer size before ' +
'performing gradient updates.')
parser.add_argument('--eval-n-steps', type=int, default=125000)
parser.add_argument('--eval-interval', type=int, default=250000)
parser.add_argument('--n-best-episodes', type=int, default=30)
args = parser.parse_args()
import logging
logging.basicConfig(level=args.logging_level)
# Set a random seed used in ChainerRL.
misc.set_random_seed(args.seed, gpus=(args.gpu,))
# Set different random seeds for train and test envs.
train_seed = args.seed
test_seed = 2 ** 31 - 1 - args.seed
args.outdir = experiments.prepare_output_dir(args, args.outdir)
print('Output files are saved in {}'.format(args.outdir))
def make_env(test):
# Use different random seeds for train and test envs
env_seed = test_seed if test else train_seed
env = atari_wrappers.wrap_deepmind(
atari_wrappers.make_atari(args.env, max_frames=None),
episode_life=not test,
clip_rewards=not test)
env.seed(int(env_seed))
if test:
# Randomize actions like epsilon-greedy in evaluation as well
env = chainerrl.wrappers.RandomizeAction(env, 0.05)
if args.monitor:
env = chainerrl.wrappers.Monitor(
env, args.outdir,
mode='evaluation' if test else 'training')
if args.render:
env = chainerrl.wrappers.Render(env)
return env
env = make_env(test=False)
eval_env = make_env(test=True)
n_actions = env.action_space.n
q_func = links.Sequence(
links.NatureDQNHead(),
L.Linear(512, n_actions),
DiscreteActionValue)
# Draw the computational graph and save it in the output directory.
chainerrl.misc.draw_computational_graph(
[q_func(np.zeros((4, 84, 84), dtype=np.float32)[None])],
os.path.join(args.outdir, 'model'))
# Use the same hyperparameters as the Nature paper
opt = optimizers.RMSpropGraves(
lr=2.5e-4, alpha=0.95, momentum=0.0, eps=1e-2)
opt.setup(q_func)
rbuf = replay_buffer.ReplayBuffer(10 ** 6)
explorer = explorers.LinearDecayEpsilonGreedy(
start_epsilon=1.0, end_epsilon=0.1,
decay_steps=10 ** 6,
random_action_func=lambda: np.random.randint(n_actions))
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
Agent = agents.DQN
agent = Agent(q_func, opt, rbuf, gpu=args.gpu, gamma=0.99,
explorer=explorer, replay_start_size=args.replay_start_size,
target_update_interval=10 ** 4,
clip_delta=True,
update_interval=4,
batch_accumulator='sum',
phi=phi)
if args.load:
agent.load(args.load)
if args.demo:
eval_stats = experiments.eval_performance(
env=eval_env,
agent=agent,
n_steps=args.eval_n_steps,
n_episodes=None)
print('n_episodes: {} mean: {} median: {} stdev {}'.format(
eval_stats['episodes'],
eval_stats['mean'],
eval_stats['median'],
eval_stats['stdev']))
else:
experiments.train_agent_with_evaluation(
agent=agent, env=env, steps=args.steps,
eval_n_steps=args.eval_n_steps,
eval_n_episodes=None,
eval_interval=args.eval_interval,
outdir=args.outdir,
save_best_so_far_agent=True,
eval_env=eval_env,
)
dir_of_best_network = os.path.join(args.outdir, "best")
agent.load(dir_of_best_network)
# run 30 evaluation episodes, each capped at 5 mins of play
stats = experiments.evaluator.eval_performance(
env=eval_env,
agent=agent,
n_steps=None,
n_episodes=args.n_best_episodes,
max_episode_len=4500,
logger=None)
with open(os.path.join(args.outdir, 'bestscores.json'), 'w') as f:
# temporary hack to handle python 2/3 support issues.
# json dumps does not support non-string literal dict keys
json_stats = json.dumps(stats)
print(str(json_stats), file=f)
print("The results of the best scoring network:")
for stat in stats:
print(str(stat) + ":" + str(stats[stat]))
if __name__ == '__main__':
main()