def demo42():
args = Arguments(if_on_policy=True)
args.agent_rl = agent.AgentGaePPO # agent.AgentPPO
import pybullet_envs # for python-bullet-gym
dir(pybullet_envs)
args.env = decorate_env(gym.make('AntBulletEnv-v0'))
args.break_step = int(5e6 * 8) # (1e6) 5e6 UsedTime: 25697s
args.reward_scale = 2 ** -3 #
args.repeat_times = 2 ** 4
args.net_dim = 2 ** 9
args.batch_size = 2 ** 8
args.max_memo = 2 ** 12
args.show_gap = 2 ** 6
args.eval_times1 = 2 ** 2
args.rollout_num = 4
train_and_evaluate__multiprocessing(args)
def demo5():
args = Arguments(if_on_policy=False)
# args.agent_rl = agent.AgentModSAC
args.agent_rl = agent.AgentInterSAC
import pybullet_envs # for python-bullet-gym
dir(pybullet_envs)
args.env = decorate_env(gym.make('AntBulletEnv-v0'))
# args.env = decorate_env(gym.make('ReacherBulletEnv-v0'))
args.break_step = int(1e6 * 8) # (5e5) 1e6, UsedTime: (15,000s) 30,000s
args.reward_scale = 2 ** -2 # (-50) 0 ~ 2500 (3340)
args.max_memo = 2 ** 19
args.net_dim = 2 ** 7 # todo
args.eva_size = 2 ** 5 # for Recorder
args.show_gap = 2 ** 8 # for Recorder
train_and_evaluate(args)