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import gym
import tensorflow as tf
from tf2rl.algos.dqn import DQN
from tf2rl.envs.atari_wrapper import wrap_dqn
from tf2rl.experiments.trainer import Trainer
from tf2rl.networks.atari_model import AtariQFunc as QFunc
if __name__ == '__main__':
parser = Trainer.get_argument()
parser = DQN.get_argument(parser)
parser.add_argument("--replay-buffer-size", type=int, default=int(1e6))
parser.add_argument('--env-name', type=str,
default="SpaceInvadersNoFrameskip-v4")
parser.set_defaults(episode_max_steps=108000)
parser.set_defaults(test_interval=10000)
parser.set_defaults(max_steps=int(1e9))
parser.set_defaults(save_model_interval=500000)
parser.set_defaults(gpu=0)
parser.set_defaults(show_test_images=True)
args = parser.parse_args()
env = wrap_dqn(gym.make(args.env_name))
test_env = wrap_dqn(gym.make(args.env_name), reward_clipping=False)
# Following parameters are equivalent to DeepMind DQN paper
# https://www.nature.com/articles/nature14236
import gym
from tf2rl.algos.sac_discrete import SACDiscrete
from tf2rl.experiments.trainer import Trainer
if __name__ == '__main__':
parser = Trainer.get_argument()
parser = SACDiscrete.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))
args = parser.parse_args()
env = gym.make("CartPole-v0")
test_env = gym.make("CartPole-v0")
policy = SACDiscrete(
state_shape=env.observation_space.shape,
action_dim=env.action_space.n,
discount=0.99,
gpu=args.gpu,
import roboschool
import gym
from tf2rl.algos.sac import SAC
from tf2rl.experiments.trainer import Trainer
if __name__ == '__main__':
parser = Trainer.get_argument()
parser = SAC.get_argument(parser)
parser.add_argument('--env-name', type=str, default="RoboschoolAnt-v1")
parser.set_defaults(batch_size=100)
parser.set_defaults(n_warmup=10000)
args = parser.parse_args()
env = gym.make(args.env_name)
test_env = gym.make(args.env_name)
policy = SAC(
state_shape=env.observation_space.shape,
action_dim=env.action_space.high.size,
gpu=args.gpu,
memory_capacity=args.memory_capacity,
max_action=env.action_space.high[0],
batch_size=args.batch_size,
n_warmup=args.n_warmup,
import roboschool
import gym
from tf2rl.algos.bi_res_ddpg import BiResDDPG
from tf2rl.experiments.trainer import Trainer
if __name__ == '__main__':
parser = Trainer.get_argument()
parser = BiResDDPG.get_argument(parser)
parser.add_argument('--env-name', type=str, default="RoboschoolAnt-v1")
parser.set_defaults(batch_size=100)
parser.set_defaults(n_warmup=10000)
args = parser.parse_args()
env = gym.make(args.env_name)
test_env = gym.make(args.env_name)
policy = BiResDDPG(
state_shape=env.observation_space.shape,
action_dim=env.action_space.high.size,
gpu=args.gpu,
eta=args.eta,
memory_capacity=args.memory_capacity,
max_action=env.action_space.high[0],
batch_size=args.batch_size,
import roboschool
import gym
from tf2rl.algos.td3 import TD3
from tf2rl.experiments.trainer import Trainer
if __name__ == '__main__':
parser = Trainer.get_argument()
parser = TD3.get_argument(parser)
parser.add_argument('--env-name', type=str, default="RoboschoolAnt-v1")
parser.set_defaults(batch_size=100)
parser.set_defaults(n_warmup=10000)
args = parser.parse_args()
env = gym.make(args.env_name)
test_env = gym.make(args.env_name)
policy = TD3(
state_shape=env.observation_space.shape,
action_dim=env.action_space.high.size,
gpu=args.gpu,
memory_capacity=args.memory_capacity,
max_action=env.action_space.high[0],
batch_size=args.batch_size,
n_warmup=args.n_warmup)
import gym
from tf2rl.algos.dqn import DQN
from tf2rl.experiments.trainer import Trainer
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 = gym.make(args.env_name)
test_env = gym.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,
def get_argument(parser=None):
parser = Trainer.get_argument(parser)
parser.add_argument('--gpu', type=int, default=0,
help='GPU id')
parser.add_argument("--max-iter", type=int, default=100)
parser.add_argument("--horizon", type=int, default=20)
parser.add_argument("--n-sample", type=int, default=1000)
parser.add_argument("--n-random-rollout", type=int, default=1000)
parser.add_argument("--batch-size", type=int, default=512)
return parser
import gym
from tf2rl.algos.categorical_dqn import CategoricalDQN
from tf2rl.experiments.trainer import Trainer
if __name__ == '__main__':
parser = Trainer.get_argument()
parser = CategoricalDQN.get_argument(parser)
parser.set_defaults(test_interval=2000)
parser.set_defaults(max_steps=int(5e5))
parser.set_defaults(gpu=-1)
args = parser.parse_args()
env = gym.make("CartPole-v0")
test_env = gym.make("CartPole-v0")
policy = CategoricalDQN(
enable_double_dqn=args.enable_double_dqn,
enable_dueling_dqn=args.enable_dueling_dqn,
state_shape=env.observation_space.shape,
action_dim=env.action_space.n,
n_warmup=500,
target_replace_interval=300,
batch_size=32,
import roboschool
import gym
from tf2rl.algos.ddpg import DDPG
from tf2rl.experiments.trainer import Trainer
if __name__ == '__main__':
parser = Trainer.get_argument()
parser = DDPG.get_argument(parser)
parser.add_argument('--env-name', type=str, default="RoboschoolAnt-v1")
parser.set_defaults(batch_size=100)
parser.set_defaults(n_warmup=10000)
args = parser.parse_args()
env = gym.make(args.env_name)
test_env = gym.make(args.env_name)
policy = DDPG(
state_shape=env.observation_space.shape,
action_dim=env.action_space.high.size,
gpu=args.gpu,
memory_capacity=args.memory_capacity,
max_action=env.action_space.high[0],
batch_size=args.batch_size,
n_warmup=args.n_warmup)