def main():
"""
Runs the test
"""
args = atari_arg_parser().parse_args()
logger.configure()
train(args.env, num_timesteps=args.num_timesteps, seed=args.seed, num_cpu=32)
def main():
"""
Runs the test
"""
parser = atari_arg_parser()
parser.add_argument('--policy', choices=['cnn', 'lstm', 'lnlstm'], default='cnn', help='Policy architecture')
parser.add_argument('--lr_schedule', choices=['constant', 'linear'], default='constant',
help='Learning rate schedule')
parser.add_argument('--logdir', help='Directory for logging')
args = parser.parse_args()
logger.configure(args.logdir)
train(args.env, num_timesteps=args.num_timesteps, seed=args.seed,
policy=args.policy, lr_schedule=args.lr_schedule, num_cpu=16)
def main():
"""
Runs the test
"""
parser = atari_arg_parser()
parser.add_argument('--policy',
help='Policy architecture',
choices=['cnn', 'lstm', 'lnlstm', 'mlp'],
default='cnn')
args = parser.parse_args()
logger.configure()
train(args.env,
num_timesteps=args.num_timesteps,
seed=args.seed,
policy=args.policy)
def main():
"""
Runs the test
"""
args = mujoco_arg_parser().parse_args()
logger.configure()
model, env = train(args.env,
num_timesteps=args.num_timesteps,
seed=args.seed)
if args.play:
logger.log("Running trained model")
obs = np.zeros((env.num_envs, ) + env.observation_space.shape)
obs[:] = env.reset()
while True:
actions = model.step(obs)[0]
obs[:] = env.step(actions)[0]
env.render()
def main():
"""
Run the atari test
"""
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--env',
help='environment ID',
default='BreakoutNoFrameskip-v4')
parser.add_argument('--seed', help='RNG seed', type=int, default=0)
parser.add_argument('--prioritized', type=int, default=1)
parser.add_argument('--dueling', type=int, default=1)
parser.add_argument('--prioritized-replay-alpha', type=float, default=0.6)
parser.add_argument('--num-timesteps', type=int, default=int(10e6))
args = parser.parse_args()
logger.configure()
set_global_seeds(args.seed)
env = make_atari(args.env)
env = Monitor(env, logger.get_dir())
env = wrap_atari_dqn(env)
policy = partial(CnnPolicy, dueling=args.dueling == 1)
model = DQN(
env=env,
policy=policy,
learning_rate=1e-4,
buffer_size=10000,
exploration_fraction=0.1,
exploration_final_eps=0.01,
train_freq=4,
learning_starts=10000,
target_network_update_freq=1000,
gamma=0.99,
prioritized_replay=bool(args.prioritized),
prioritized_replay_alpha=args.prioritized_replay_alpha,
)
model.learn(total_timesteps=args.num_timesteps)
env.close()