def demo2_continuous_action_space_off_policy():
args = Arguments(if_on_policy=False)
'''choose an DRL algorithm'''
from elegantrl.agent import AgentModSAC # AgentSAC, AgentTD3, AgentDDPG
args.agent = AgentModSAC()
'''choose environment'''
"TotalStep: 3e4, TargetReturn: -200, UsedTime: 300s, Pendulum-v0, TD3"
"TotalStep: 2e4, TargetReturn: -200, UsedTime: 200s, Pendulum-v0, ModSAC "
env = gym.make('Pendulum-v0')
env.target_return = -200 # set target_return manually for env 'Pendulum-v0'
args.env = PreprocessEnv(env=env)
args.reward_scale = 2 ** -3 # RewardRange: -1800 < -200 < -50 < 0
"TD3 TotalStep: 9e4, TargetReturn: 100, UsedTime: 3ks, LunarLanderContinuous-v2"
"TD3 TotalStep: 20e4, TargetReturn: 200, UsedTime: 5ks, LunarLanderContinuous-v2"
"SAC TotalStep: 9e4, TargetReturn: 200, UsedTime: 3ks, LunarLanderContinuous-v2"
"ModSAC TotalStep: 5e4, TargetReturn: 200, UsedTime: 1ks, LunarLanderContinuous-v2"
# args.env = PreprocessEnv(env=gym.make('LunarLanderContinuous-v2'))
# args.reward_scale = 2 ** 0 # RewardRange: -800 < -200 < 200 < 302
# args.eval_times2 = 2 ** 4 # set a large eval_times to get a precise learning curve
"ModSAC TotalStep: 2e5, TargetReturn: 300, UsedTime: 5000s, BipedalWalker-v3"
# args.env = PreprocessEnv(env=gym.make('BipedalWalker-v3'))
# args.reward_scale = 2 ** 0 # RewardRange: -200 < -150 < 300 < 334
# args.net_dim = 2 ** 8
# args.break_step = int(2e5)
# args.if_allow_break = True # allow break training when reach goal (early termination)
# args.break_step = int(2e5 * 4) # break training after 'total_step > break_step'
'''train and evaluate'''
train_and_evaluate(args)
def check_stock_trading_env():
if_eval = True # False
env = StockTradingEnv(if_eval=if_eval)
action_dim = env.action_dim
state = env.reset()
print('state_dim', len(state))
done = False
step = 1
reward = None
from time import time
timer = time()
while not done:
action = rd.rand(action_dim) * 2 - 1
next_state, reward, done, _ = env.step(action)
# print(';', len(next_state), env.day, reward)
step += 1
print(f"step: {step}, UsedTime: {time() - timer:.3f}")
print(f"terminal reward {reward:.3f}")
print(f"episode return {env.episode_return:.3f}")
'''draw_cumulative_return'''
from elegantrl.agent import AgentPPO
from elegantrl.run import Arguments
args = Arguments(if_on_policy=True)
args.agent = AgentPPO()
args.init_before_training()
args.agent.save_load_model(cwd='./AgentPPO/StockTradingEnv-v1_0',
if_save=False)
env.draw_cumulative_return(args, torch)
def demo2_continuous_action_space_on_policy():
args = Arguments(if_on_policy=True) # hyper-parameters of on-policy is different from off-policy
args.random_seed = 1943
'''choose an DRL algorithm'''
from elegantrl.agent import AgentPPO
args.agent = AgentPPO()
args.agent.if_use_gae = False
'''choose environment'''
"TotalStep: 2e5, TargetReturn: -200, UsedTime: 300s, Pendulum-v0, PPO"
env = gym.make('Pendulum-v0')
env.target_return = -200 # set target_return manually for env 'Pendulum-v0'
args.env = PreprocessEnv(env=env)
args.reward_scale = 2 ** -3 # RewardRange: -1800 < -200 < -50 < 0
args.repeat_times = 2 ** 3
args.target_step = 200 * 8
args.eval_gap = 2 ** 6
"PPO TotalStep: 8e5, TargetReturn: 200, UsedTime: 1500s, LunarLanderContinuous-v2"
# args.env = PreprocessEnv(env=gym.make('LunarLanderContinuous-v2'))
# args.reward_scale = 2 ** 0 # RewardRange: -800 < -200 < 200 < 302
"PPO TotalStep: 8e5, TargetReturn: 300, UsedTime: 1800s, BipedalWalker-v3"
# args.env = PreprocessEnv(env=gym.make('BipedalWalker-v3'))
# args.reward_scale = 2 ** 0 # RewardRange: -200 < -150 < 300 < 334
# args.gamma = 0.96
'''train and evaluate'''
# train_and_evaluate(args)
args.rollout_num = 2
train_and_evaluate_mp(args)
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)
from elegantrl.agent import AgentPPO
import gym
from elegantrl.env import PreprocessEnv
from elegantrl.run import Arguments, train_and_evaluate, train_and_evaluate_mp
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
def __init__(self,
ticker_list,
time_interval,
drl_lib,
agent,
cwd,
net_dim,
state_dim,
action_dim,
API_KEY,
API_SECRET,
APCA_API_BASE_URL,
tech_indicator_list,
turbulence_thresh=30,
max_stock=1e2,
latency=None):
#load agent
self.drl_lib = drl_lib
if agent == 'ppo':
if drl_lib == 'elegantrl':
from elegantrl.agent import AgentPPO
from elegantrl.run import Arguments, init_agent
#load agent
config = {
'state_dim': state_dim,
'action_dim': action_dim,
}
args = Arguments(agent=AgentPPO, env=StockEnvEmpty(config))
args.cwd = cwd
args.net_dim = net_dim
# load agent
try:
agent = init_agent(args, gpu_id=0)
self.act = agent.act
self.device = agent.device
except BaseException:
raise ValueError("Fail to load agent!")
elif drl_lib == 'rllib':
from ray.rllib.agents import ppo
from ray.rllib.agents.ppo.ppo import PPOTrainer
config = ppo.DEFAULT_CONFIG.copy()
config['env'] = StockEnvEmpty
config["log_level"] = "WARN"
config['env_config'] = {
'state_dim': state_dim,
'action_dim': action_dim,
}
trainer = PPOTrainer(env=StockEnvEmpty, config=config)
trainer.restore(cwd)
try:
trainer.restore(cwd)
self.agent = trainer
print("Restoring from checkpoint path", cwd)
except:
raise ValueError('Fail to load agent!')
elif drl_lib == 'stable_baselines3':
from stable_baselines3 import PPO
try:
#load agent
self.model = PPO.load(cwd)
print("Successfully load model", cwd)
except:
raise ValueError('Fail to load agent!')
else:
raise ValueError(
'The DRL library input is NOT supported yet. Please check your input.'
)
else:
raise ValueError('Agent input is NOT supported yet.')
#connect to Alpaca trading API
try:
self.alpaca = tradeapi.REST(API_KEY, API_SECRET, APCA_API_BASE_URL,
'v2')
except:
raise ValueError(
'Fail to connect Alpaca. Please check account info and internet connection.'
)
#read trading time interval
if time_interval == '1s':
self.time_interval = 1
elif time_interval == '5s':
self.time_interval = 5
elif time_interval == '1Min':
self.time_interval = 60
elif time_interval == '5Min':
self.time_interval = 60 * 5
elif time_interval == '15Min':
self.time_interval = 60 * 15
else:
raise ValueError('Time interval input is NOT supported yet.')
#read trading settings
self.tech_indicator_list = tech_indicator_list
self.turbulence_thresh = turbulence_thresh
self.max_stock = max_stock
#initialize account
self.stocks = np.asarray([0] * len(ticker_list)) #stocks holding
self.stocks_cd = np.zeros_like(self.stocks)
self.cash = None #cash record
self.stocks_df = pd.DataFrame(self.stocks,
columns=['stocks'],
index=ticker_list)
self.asset_list = []
self.price = np.asarray([0] * len(ticker_list))
self.stockUniverse = ticker_list
self.turbulence_bool = 0
self.equities = []
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 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)
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 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)