def demo1_discrete_action_space():
args = Arguments(agent=None, env=None, gpu_id=None) # see Arguments() to see hyper-parameters
'''choose an DRL algorithm'''
# from elegantrl.agent import AgentD3QN # AgentDQN,AgentDuelDQN, AgentDoubleDQN,
# args.agent = AgentD3QN()
from elegantrl.agent import AgentDuelingDQN # AgentDQN,AgentDuelDQN, AgentDoubleDQN,
args.agent = AgentDuelingDQN()
'''choose environment'''
"TotalStep: 2e3, TargetReturn: 200, UsedTime: 20s, CartPole-v0"
"TotalStep: 2e3, TargetReturn: 200, UsedTime: 30s, CartPole-v0 rollout_num = 2"
# args.env = PreprocessEnv(env=gym.make('CartPole-v0'))
# args.net_dim = 2 ** 7 # change a default hyper-parameters
# args.batch_size = 2 ** 7
# args.target_step = 2 ** 8
# args.eval_gap = 2 ** 0
"TotalStep: 6e4, TargetReturn: 200, UsedTime: 600s, LunarLander-v2, D3DQN"
"TotalStep: 4e4, TargetReturn: 200, UsedTime: 600s, LunarLander-v2, DuelDQN"
args.env = PreprocessEnv(env=gym.make('LunarLander-v2'))
args.net_dim = 2 ** 8
args.batch_size = 2 ** 8
'''train and evaluate'''
train_and_evaluate(args)
def demo3_custom_env_fin_rl():
from elegantrl.agent import AgentPPO
'''choose an DRL algorithm'''
args = Arguments(if_on_policy=True)
args.agent = AgentPPO()
args.agent.if_use_gae = False
"TotalStep: 5e4, TargetReturn: 1.25, UsedTime: 20s, FinanceStock-v2"
"TotalStep: 20e4, TargetReturn: 1.50, UsedTime: 80s, FinanceStock-v2"
from elegantrl.env import FinanceStockEnv # a standard env for ElegantRL, not need PreprocessEnv()
args.env = FinanceStockEnv(if_train=True, train_beg=0, train_len=1024)
args.env_eval = FinanceStockEnv(if_train=False, train_beg=0, train_len=1024) # eva_len = 1699 - train_len
args.reward_scale = 2 ** 0 # RewardRange: 0 < 1.0 < 1.25 < 1.5 < 1.6
args.break_step = int(5e6)
args.net_dim = 2 ** 8
args.max_step = args.env.max_step
args.max_memo = (args.max_step - 1) * 8
args.batch_size = 2 ** 11
args.repeat_times = 2 ** 4
args.eval_times1 = 2 ** 2
args.eval_times2 = 2 ** 4
args.if_allow_break = True
'''train and evaluate'''
# train_and_evaluate(args)
args.rollout_num = 8
train_and_evaluate_mp(args)
def demo3_custom_env_fin_rl():
from elegantrl.agent import AgentPPO
'''choose an DRL algorithm'''
args = Arguments(if_on_policy=True)
args.agent = AgentPPO()
args.agent.if_use_gae = True
args.agent.lambda_entropy = 0.04
"TotalStep: 10e4, TargetReturn: 3.0, UsedTime: 200s, FinanceStock-v1"
"TotalStep: 20e4, TargetReturn: 4.0, UsedTime: 400s, FinanceStock-v1"
"TotalStep: 30e4, TargetReturn: 4.2, UsedTime: 600s, FinanceStock-v1"
from envs.FinRL.StockTrading import StockTradingEnv
gamma = 0.995
args.env = StockTradingEnv(if_eval=False, gamma=gamma)
args.env_eval = StockTradingEnv(if_eval=True, gamma=gamma)
args.gamma = gamma
args.break_step = int(3e5)
args.net_dim = 2**9
args.max_step = args.env.max_step
args.max_memo = args.max_step * 4
args.batch_size = 2**10
args.repeat_times = 2**3
args.eval_gap = 2**4
args.eval_times1 = 2**3
args.eval_times2 = 2**5
args.if_allow_break = False
'''train and evaluate'''
# train_and_evaluate(args)
args.rollout_num = 4
train_and_evaluate_mp(args)
def demo4_bullet_mujoco_on_policy():
args = Arguments(if_on_policy=True) # hyper-parameters of on-policy is different from off-policy
import pybullet_envs # for python-bullet-gym
dir(pybullet_envs)
"TotalStep: 1e5, TargetReturn: 18, UsedTime: 3ks, ReacherBulletEnv-v0, PPO"
"TotalStep: 1e6, TargetReturn: 18, UsedTime: 30ks, ReacherBulletEnv-v0, PPO"
args.env = PreprocessEnv(gym.make('ReacherBulletEnv-v0'))
from elegantrl.agent import AgentPPO
args.agent = AgentPPO()
args.agent.if_use_gae = True
args.break_step = int(2e5 * 8)
args.reward_scale = 2 ** 0 # RewardRange: -15 < 0 < 18 < 25
args.gamma = 0.96
args.eval_times1 = 2 ** 2
args.eval_times1 = 2 ** 5
# train_and_evaluate(args)
args.rollout_num = 4
train_and_evaluate_mp(args)
"TotalStep: 3e6, TargetReturn: 1500, UsedTime: 2ks, AntBulletEnv-v0, PPO"
"TotalStep: 10e6, TargetReturn: 2500, UsedTime: 6ks, AntBulletEnv-v0, PPO"
"TotalStep: 46e6, TargetReturn: 3017, UsedTime: 25ks, AntBulletEnv-v0, PPO"
"TotalStep: 5e6, TargetReturn: 1500, UsedTime: 3ks, AntBulletEnv-v0, PPO if_use_dn"
"TotalStep: 15e6, TargetReturn: 2500, UsedTime: 10ks, AntBulletEnv-v0, PPO if_use_dn"
"TotalStep: 60e6, TargetReturn: 2949, UsedTime: 34ks, AntBulletEnv-v0, PPO if_use_dn"
"TotalStep: 2e6, TargetReturn: 1500, UsedTime: 2ks, AntBulletEnv-v0, PPO if_use_cn"
"TotalStep: 10e6, TargetReturn: 2500, UsedTime: 7ks, AntBulletEnv-v0, PPO if_use_cn"
"TotalStep: 53e6, TargetReturn: 2834, UsedTime: 35ks, AntBulletEnv-v0, PPO if_use_cn"
args.env = PreprocessEnv(env=gym.make('AntBulletEnv-v0'))
from elegantrl.agent import AgentPPO
args.agent = AgentPPO()
args.agent.if_use_gae = True
args.agent.lambda_entropy = 0.05
args.agent.lambda_gae_adv = 0.97
args.if_allow_break = False
args.break_step = int(8e6 * 8) # (5e5) 1e6, UsedTime: (15,000s) 30,000s
args.reward_scale = 2 ** -2 # (-50) 0 ~ 2500 (3340)
args.max_memo = args.env.max_step * 4
args.batch_size = 2 ** 11 # 10
args.repeat_times = 2 ** 3
args.eval_gap = 2 ** 8 # for Recorder
args.eva_size1 = 2 ** 1 # for Recorder
args.eva_size2 = 2 ** 3 # for Recorder
# train_and_evaluate(args)
args.rollout_num = 4
train_and_evaluate_mp(args)
def demo3_custom_env_fin_rl():
from elegantrl.agent import AgentPPO
'''choose an DRL algorithm'''
args = Arguments(if_on_policy=True)
args.agent = AgentPPO()
args.agent.if_use_gae = False
"TotalStep: 5e4, TargetReturn: 1.25, UsedTime: 20s, FinanceStock-v2"
"TotalStep: 20e4, TargetReturn: 1.50, UsedTime: 80s, FinanceStock-v2"
# from elegantrl.env import FinanceStockEnv # a standard env for ElegantRL, not need PreprocessEnv()
# args.env = FinanceStockEnv(if_train=True, train_beg=0, train_len=1024)
# args.env_eval = FinanceStockEnv(if_train=False, train_beg=0, train_len=1024) # eva_len = 1699 - train_len
from finrl.config import config
from beta3 import StockTradingEnv, load_stock_trading_data
train_df, eval_df = load_stock_trading_data()
# train = data_split(processed_df, config.START_DATE, config.START_TRADE_DATE)
# trade = data_split(processed_df, config.START_TRADE_DATE, config.END_DATE)
# calculate state action space
stock_dimension = len(train_df.tic.unique())
state_space = 1 + (2 +
len(config.TECHNICAL_INDICATORS_LIST)) * stock_dimension
env_kwargs = {
"max_stock": 100,
"initial_amount": 1000000,
"buy_cost_pct": 0.001,
"sell_cost_pct": 0.001,
"state_space": state_space,
"stock_dim": stock_dimension,
"tech_indicator_list": config.TECHNICAL_INDICATORS_LIST,
"action_space": stock_dimension,
"reward_scaling": 2**-14
}
args.env = StockTradingEnv(df=train_df, **env_kwargs)
args.env_eval = StockTradingEnv(df=eval_df, **env_kwargs)
args.reward_scale = 2**0 # RewardRange: 0 < 1.0 < 1.25 < 1.5 < 1.6
args.break_step = int(5e6)
args.net_dim = 2**8
args.max_step = args.env.max_step
args.max_memo = (args.max_step - 1) * 8
args.batch_size = 2**11
args.repeat_times = 2**4
args.eval_times1 = 2**1
args.eval_times2 = 2**3
args.if_allow_break = True
'''train and evaluate'''
# train_and_evaluate(args)
args.rollout_num = 4
train_and_evaluate_mp(args)
def demo4_bullet_mujoco_off_policy():
args = Arguments(if_on_policy=False)
args.random_seed = 10086
from elegantrl.agent import AgentModSAC # AgentSAC, AgentTD3, AgentDDPG
args.agent = AgentModSAC() # AgentSAC(), AgentTD3(), AgentDDPG()
args.agent.if_use_dn = True
import pybullet_envs # for python-bullet-gym
dir(pybullet_envs)
"TotalStep: 5e4, TargetReturn: 18, UsedTime: 1100s, ReacherBulletEnv-v0"
"TotalStep: 30e4, TargetReturn: 25, UsedTime: s, ReacherBulletEnv-v0"
args.env = PreprocessEnv(gym.make('ReacherBulletEnv-v0'))
args.env.max_step = 2 ** 10 # important, default env.max_step=150
args.reward_scale = 2 ** 0 # -80 < -30 < 18 < 28
args.gamma = 0.96
args.break_step = int(6e4 * 8) # (4e4) 8e5, UsedTime: (300s) 700s
args.eval_times1 = 2 ** 2
args.eval_times1 = 2 ** 5
args.if_per = True
train_and_evaluate(args)
"TotalStep: 3e5, TargetReward: 1500, UsedTime: 4ks, AntBulletEnv-v0 ModSAC if_use_dn"
"TotalStep: 4e5, TargetReward: 2500, UsedTime: 6ks, AntBulletEnv-v0 ModSAC if_use_dn"
"TotalStep: 10e5, TargetReward: 2879, UsedTime: ks, AntBulletEnv-v0 ModSAC if_use_dn"
"TotalStep: 3e5, TargetReward: 1500, UsedTime: 8ks, AntBulletEnv-v0 ModSAC if_use_cn"
"TotalStep: 7e5, TargetReward: 2500, UsedTime: 18ks, AntBulletEnv-v0 ModSAC if_use_cn"
"TotalStep: 16e5, TargetReward: 2923, UsedTime: ks, AntBulletEnv-v0 ModSAC if_use_cn"
args.env = PreprocessEnv(env=gym.make('AntBulletEnv-v0'))
args.break_step = int(6e5 * 8) # (5e5) 1e6, UsedTime: (15,000s) 30,000s
args.if_allow_break = False
args.reward_scale = 2 ** -2 # RewardRange: -50 < 0 < 2500 < 3340
args.max_memo = 2 ** 21
args.batch_size = 2 ** 8
args.repeat_times = 2 ** 1
args.eval_gap = 2 ** 9 # for Recorder
args.eva_size1 = 2 ** 1 # for Recorder
args.eva_size2 = 2 ** 3 # for Recorder
# train_and_evaluate(args)
args.rollout_num = 4
train_and_evaluate_mp(args)
if __name__ == '__main__':
args = Arguments(if_on_policy=True)
args.agent = AgentPPO()
args.agent.if_use_gae = True
args.agent.lambda_entropy = 0.04
from kuka_cam_reach_env import KukaCamReachEnv, CustomSkipFrame
env_config = {
"is_render": False,
"is_good_view": False,
"max_steps_one_episode": 1000,
}
args.env = CustomSkipFrame(KukaCamReachEnv(config=env_config))
args.gamma = 0.995
args.break_step = int(3e5)
args.net_dim = 2**9
args.max_step = args.env.max_step
args.max_memo = args.max_step * 4
args.batch_size = 2**10
args.repeat_times = 2**3
args.eval_gap = 2**4
args.eval_times1 = 2**3
args.eval_times2 = 2**5
args.if_allow_break = False
'''train and evaluate'''
# train_and_evaluate(args)
args.rollout_num = 1
train_and_evaluate(args)