def run(args):
env = make_dmc_env(args.domain_name, args.task_name, args.action_repeat)
env_test = make_dmc_env(args.domain_name, args.task_name, args.action_repeat)
algo = SAC_AE(
num_agent_steps=args.num_agent_steps,
state_space=env.observation_space,
action_space=env.action_space,
seed=args.seed,
)
time = datetime.now().strftime("%Y%m%d-%H%M")
log_dir = os.path.join("logs", f"{args.domain_name}-{args.task_name}", f"{str(algo)}-seed{args.seed}-{time}")
trainer = Trainer(
env=env,
env_test=env_test,
algo=algo,
log_dir=log_dir,
num_agent_steps=args.num_agent_steps,
action_repeat=args.action_repeat,
eval_interval=args.eval_interval,
seed=args.seed,
)
trainer.train()
def run(args):
env = make_continuous_env(args.env_id)
env_test = make_continuous_env(args.env_id)
algo = DDPG(
num_agent_steps=args.num_agent_steps,
state_space=env.observation_space,
action_space=env.action_space,
seed=args.seed,
)
time = datetime.now().strftime("%Y%m%d-%H%M")
log_dir = os.path.join("logs", args.env_id,
f"{str(algo)}-seed{args.seed}-{time}")
trainer = Trainer(
env=env,
env_test=env_test,
algo=algo,
log_dir=log_dir,
num_agent_steps=args.num_agent_steps,
eval_interval=args.eval_interval,
seed=args.seed,
)
trainer.train()
def run(args):
env = make_atari_env(args.env_id, sign_rewards=False, clip_rewards=True)
env_test = make_atari_env(args.env_id,
episode_life=False,
sign_rewards=False)
algo = SAC_Discrete(
num_agent_steps=args.num_agent_steps,
state_space=env.observation_space,
action_space=env.action_space,
seed=args.seed,
)
time = datetime.now().strftime("%Y%m%d-%H%M")
log_dir = os.path.join("logs", args.env_id,
f"{str(algo)}-seed{args.seed}-{time}")
trainer = Trainer(
env=env,
env_test=env_test,
algo=algo,
log_dir=log_dir,
num_agent_steps=args.num_agent_steps,
action_repeat=4,
eval_interval=args.eval_interval,
seed=args.seed,
)
trainer.train()