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plan_PPO_H.py
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plan_PPO_H.py
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import sys
import gym
from elegantrl.train.run import train_and_evaluate, train_and_evaluate_mp
from elegantrl.train.config import Arguments
from elegantrl.agents.AgentPPO import AgentPPO, AgentPPOHterm
from elegantrl.envs.CustomGymEnv import GymNormaEnv
def demo_ppo_h_term(gpu_id, drl_id, env_id):
env_name = ['Hopper-v3',
'Swimmer-v3',
'Ant-v3',
'Humanoid-v3',
'HalfCheetah-v3',
'Walker2d-v3',
][env_id]
agent_class = [AgentPPO, AgentPPOHterm][drl_id]
if env_name == 'Hopper-v3':
env_func = GymNormaEnv
env_args = {
'num_envs': 1,
'env_name': 'Hopper-v3',
'max_step': 1000,
'state_dim': 11,
'action_dim': 3,
'if_discrete': False,
'target_return': 3500.,
}
args = Arguments(agent_class, env_func=env_func, env_args=env_args)
args.num_layer = 3
args.net_dim = 2 ** 8
args.batch_size = int(args.net_dim * 2)
args.worker_num = 4
args.target_step = args.max_step * 1
args.repeat_times = 2 ** 4
args.learning_rate = 2 ** -15
args.clip_grad_norm = 1.0
args.reward_scale = 2 ** -4
args.gamma = 0.993
args.lambda_gae_adv = 0.95
args.lambda_entropy = 0.05
args.if_use_gae = True
args.ratio_clip = 0.20
'''H-term'''
args.h_term_drop_rate = 2 ** -2
args.h_term_lambda = 2 ** -3
args.act_update_gap = 1
args.h_term_k_step = 64 # 16
args.eval_times = 2 ** 1
args.eval_gap = 2 ** 8
args.if_allow_break = False
args.break_step = int(2e6)
elif env_name == 'Swimmer-v3':
# env_func = GymNormaEnv # gym.make
env_func = gym.make
env_args = {
'action_dim': 2,
'env_name': 'Swimmer-v3',
'num_envs': 1,
'if_discrete': False,
'max_step': 1000,
'state_dim': 8,
'target_return': 360.0
}
args = Arguments(agent_class, env_func=env_func, env_args=env_args)
args.num_layer = 3
args.net_dim = 2 ** 8
args.batch_size = int(args.net_dim * 2)
args.worker_num = 4
args.target_step = args.max_step * 1
args.repeat_times = 2 ** 4
args.learning_rate = 2 ** -14
args.clip_grad_norm = 3.0
args.reward_scale = 2 ** -1
args.gamma = 0.993
args.lambda_gae_adv = 0.95
args.lambda_entropy = 0.05
args.if_use_gae = True
args.ratio_clip = 0.20
args.repeat_times = 2 ** 2
'''H-term'''
args.h_term_drop_rate = 2 ** -2
args.h_term_lambda = 2 ** -3
args.act_update_gap = 1
args.h_term_k_step = 64 # 16
args.eval_times = 2 ** 1
args.eval_gap = 2 ** 8
args.if_allow_break = False
elif env_name == 'Ant-v3':
env_func = GymNormaEnv
env_args = {
'num_envs': 1,
'env_name': 'Ant-v3',
'max_step': 1000,
'state_dim': 111,
'action_dim': 8,
'if_discrete': False,
'target_return': 6000.0,
}
args = Arguments(agent_class, env_func=env_func, env_args=env_args)
args.reward_scale = 2 ** -4
args.num_layer = 3
args.net_dim = 2 ** 8
args.batch_size = int(args.net_dim * 2)
args.worker_num = 4
args.target_step = args.max_step * 1
args.repeat_times = 2 ** 5
args.reward_scale = 2 ** -4
args.learning_rate = 2 ** -15
args.clip_grad_norm = 0.6
args.gamma = 0.99
args.clip_grad_norm = 2.0
args.lambda_gae_adv = 0.94
args.lambda_entropy = 2 ** -6
args.if_use_gae = True
args.ratio_clip = 0.3
'''H-term'''
args.h_term_drop_rate = 2 ** -3
args.h_term_lambda = 2 ** -3
args.h_term_k_step = 12
args.save_gap = 2 ** 4
args.eval_gap = 2 ** 9
args.eval_times = 2 ** 1
args.break_step = int(2e7)
args.if_allow_break = False
elif env_name == 'Humanoid-v3':
from rl.envs.CustomGymEnv import HumanoidEnv
env_func = HumanoidEnv
env_args = {
'num_envs': 1,
'env_name': 'Humanoid-v3',
'max_step': 1000,
'state_dim': 376,
'action_dim': 17,
'if_discrete': False,
'target_return': 8000.,
}
args = Arguments(agent_class, env_func=env_func, env_args=env_args)
args.num_layer = 3
args.net_dim = 2 ** 9 #
args.batch_size = int(args.net_dim * 2)
args.worker_num = 8
args.target_step = args.max_step * 3
args.repeat_times = 2 ** 4 #
args.learning_rate = 2 ** -15
args.clip_grad_norm = 1.0
args.reward_scale = 2 ** -3
args.gamma = 0.985
args.lambda_gae_adv = 0.94
args.lambda_entropy = 2 ** -2
args.if_use_gae = True
args.ratio_clip = 0.30
'''H-term'''
args.h_term_drop_rate = 2 ** -2
args.h_term_lambda = 2 ** -3
args.h_term_k_step = 16
args.save_gap = 2 ** 3
args.eval_gap = 2 ** 9
args.eval_times = 2 ** 2
args.if_allow_break = False
args.break_step = int(2e8)
elif env_name == 'HalfCheetah-v3':
env_func = GymNormaEnv
env_args = {
'num_envs': 1,
'env_name': 'HalfCheetah-v3',
'max_step': 1000,
'state_dim': 17,
'action_dim': 6,
'if_discrete': False,
'target_return': 4800.0,
}
args = Arguments(agent_class, env_func=env_func, env_args=env_args)
args.num_layer = 3
args.net_dim = 2 ** 7
args.batch_size = int(args.net_dim * 2)
args.worker_num = 4
args.target_step = args.max_step * 1
args.repeat_times = 2 ** 5
args.learning_rate = 2 ** -15
args.clip_grad_norm = 0.8
args.reward_scale = 2 ** -5
args.gamma = 0.99
args.lambda_gae_adv = 0.94
args.lambda_entropy = 2 ** -9
args.if_use_gae = True
args.ratio_clip = 0.3
'''H-term'''
args.h_term_drop_rate = 2 ** -2
args.h_term_lambda = 2 ** -3
args.h_term_k_step = 4
args.eval_gap = 2 ** 8
args.eval_times = 2 ** 1
args.if_allow_break = False
args.break_step = int(2e7)
elif env_name == 'Walker2d-v3':
env_func = GymNormaEnv
env_args = {
'num_envs': 1,
'env_name': 'Walker2d-v3',
'if_discrete': False,
'max_step': 1000,
'state_dim': 17,
'action_dim': 6,
'target_return': 65536
}
args = Arguments(agent_class, env_func=env_func, env_args=env_args)
args.num_layer = 3
args.net_dim = 2 ** 8
args.batch_size = int(args.net_dim * 2)
args.worker_num = 4
args.target_step = args.max_step * 2
args.repeat_times = 2 ** 5
args.reward_scale = 2 ** -3
args.learning_rate = 2 ** -15
args.clip_grad_norm = 2.0
args.gamma = 0.985
args.lambda_gae_adv = 0.94
args.if_use_gae = True
args.ratio_clip = 0.4
args.lambda_entropy = 2 ** -4
if gpu_id == 2:
args.ratio_clip = 0.6
if gpu_id == 3:
args.ratio_clip = 0.4
'''H-term'''
args.h_term_sample_rate = 2 ** -2
args.h_term_drop_rate = 2 ** -3
args.h_term_lambda = 2 ** -3
args.h_term_k_step = 12
args.save_gap = 2 ** 3
args.eval_gap = 2 ** 8
args.eval_times = 2 ** 1
args.if_allow_break = False
args.break_step = int(2e7)
else:
raise ValueError('env_name:', env_name)
args.learner_gpu_ids = gpu_id
args.random_seed += gpu_id + 19431
if_check = 0
if if_check:
train_and_evaluate(args)
else:
train_and_evaluate_mp(args)
if __name__ == '__main__':
GPU_ID = int(sys.argv[1]) if len(sys.argv) > 1 else 0 # >=0 means GPU ID, -1 means CPU
AGENT_ID = int(sys.argv[2]) if len(sys.argv) > 2 else 1
ENV_ID = int(sys.argv[3]) if len(sys.argv) > 3 else 1
demo_ppo_h_term(GPU_ID, AGENT_ID, ENV_ID)