if __name__ == '__main__':
parser = Trainer.get_argument()
parser = DQN.get_argument(parser)
parser.set_defaults(test_interval=2000)
parser.set_defaults(max_steps=100000)
parser.set_defaults(gpu=-1)
parser.set_defaults(n_warmup=500)
parser.set_defaults(batch_size=32)
parser.set_defaults(memory_capacity=int(1e4))
parser.add_argument('--env-name', type=str, default="CartPole-v0")
args = parser.parse_args()
env = make(args.env_name)
test_env = make(args.env_name)
policy = DQN(enable_double_dqn=args.enable_double_dqn,
enable_dueling_dqn=args.enable_dueling_dqn,
enable_noisy_dqn=args.enable_noisy_dqn,
state_shape=env.observation_space.shape,
action_dim=env.action_space.n,
target_replace_interval=300,
discount=0.99,
gpu=args.gpu,
memory_capacity=args.memory_capacity,
batch_size=args.batch_size,
n_warmup=args.n_warmup)
trainer = Trainer(policy, env, args, test_env=test_env)
if args.evaluate:
trainer.evaluate_policy_continuously()
else:
trainer()
def main():
dm_envs = {
'finger': ['finger', 'spin', 2],
'cartpole': ['cartpole', 'swingup', 8],
'reacher': ['reacher', 'easy', 4],
'cheetah': ['cheetah', 'run', 4],
'walker': ['walker', 'walk', 2],
'ball': ['ball_in_cup', 'catch', 4],
'humanoid': ['humanoid', 'stand', 4],
'bring_ball': ['manipulator', 'bring_ball', 4],
'bring_peg': ['manipulator', 'bring_peg', 4],
'insert_ball': ['manipulator', 'insert_ball', 4],
'insert_peg': ['manipulator', 'insert_peg', 4]}
parser = Trainer.get_argument()
parser = CURL.get_argument(parser)
parser.add_argument('--env-name', type=str, default="cartpole", choices=dm_envs.keys())
parser.add_argument('--seed', type=int, default=1)
parser.set_defaults(save_summary_interval=50)
parser.set_defaults(memory_capacity=int(1e5))
args = parser.parse_args()
domain_name, task_name, action_repeat = dm_envs[args.env_name]
original_obs_shape = (100, 100, 9)
input_obs_shape = (84, 84, 9)
def make_env():
return DMCWrapper(
dmc2gym.make(
domain_name=domain_name,
task_name=task_name,
seed=args.seed,
visualize_reward=False,
from_pixels=True,
height=100,
width=100,
frame_skip=action_repeat,
channels_first=False),
obs_shape=original_obs_shape,
k=3,
channel_first=False)
env = make_env()
test_env = make_env()
# see Table 3 of CURL paper
lr_sac = lr_encoder = 2e-4 if args.env_name == "cheetah" else 1e-3
policy = CURL(
obs_shape=input_obs_shape,
action_dim=env.action_space.high.size,
gpu=args.gpu,
memory_capacity=args.memory_capacity,
n_warmup=int(1e3),
max_action=env.action_space.high[0],
batch_size=512,
actor_units=(1024, 1024),
critic_units=(1024, 1024),
lr_sac=lr_sac,
lr_encoder=lr_encoder,
lr_alpha=1e-4,
tau_critic=0.01,
init_temperature=0.1,
auto_alpha=True,
stop_q_grad=args.stop_q_grad)
trainer = Trainer(policy, env, args, test_env=test_env)
if args.evaluate:
trainer.evaluate_policy_continuously()
else:
trainer()
def main():
dm_envs = {
'finger': ['finger', 'spin', 2],
'cartpole': ['cartpole', 'balance', 4],
'reacher': ['reacher', 'easy', 4],
'cheetah': ['cheetah', 'run', 4],
'walker': ['walker', 'walk', 2],
'ball': ['ball_in_cup', 'catch', 4],
'humanoid': ['humanoid', 'stand', 4],
'bring_ball': ['manipulator', 'bring_ball', 4],
'bring_peg': ['manipulator', 'bring_peg', 4],
'insert_ball': ['manipulator', 'insert_ball', 4],
'insert_peg': ['manipulator', 'insert_peg', 4]
}
parser = Trainer.get_argument()
parser = CURLSAC.get_argument(parser)
parser.add_argument('--env-name',
type=str,
default="cartpole",
choices=dm_envs.keys())
parser.add_argument('--seed', type=int, default=1)
parser.set_defaults(batch_size=256)
parser.set_defaults(n_warmup=10000)
parser.set_defaults(max_steps=3e6)
parser.set_defaults(save_summary_interval=100)
args = parser.parse_args()
domain_name, task_name, action_repeat = dm_envs[args.env_name]
original_obs_shape = (100, 100, 9)
input_obs_shape = (84, 84, 9)
def make_env():
return DMCWrapper(dmc2gym.make(domain_name=domain_name,
task_name=task_name,
seed=args.seed,
visualize_reward=False,
from_pixels=True,
height=100,
width=100,
frame_skip=action_repeat,
channels_first=False),
obs_shape=original_obs_shape,
k=3,
channel_first=False)
env = make_env()
test_env = make_env()
policy = CURLSAC(obs_shape=input_obs_shape,
action_dim=env.action_space.high.size,
gpu=args.gpu,
memory_capacity=int(1e5),
n_warmup=int(1e3),
max_action=env.action_space.high[0],
batch_size=10,
alpha=args.alpha,
auto_alpha=args.auto_alpha)
trainer = Trainer(policy, env, args, test_env=test_env)
if args.evaluate:
trainer.evaluate_policy_continuously()
else:
trainer()