Exception in agent.train_model: no description

[zhipentian]

Follow the tutorial https://github.com/AI4Finance-Foundation/FinRL-Tutorials/blob/master/3-Practical/FinRL_MultiCrypto_Trading.ipynb .
Always in:
Trained_model=agent.train_model（
Model=model, cwd=cwd, total_timesteps=break_step
）
Error:
exception: no description
File "D:\per\AI\test1\traintest.py", line 66, in train
trained_model = agent.train_model(
^^^^^^^^^^^^^^^^^^
File "D:\per\AI\test1\Crypto.py", line 32, in
train(start_date=TRAIN_START_DATE,
AssertionError:

Please help!!!
Source code：
Cypto.py

from meta.env_crypto_trading.env_multiple_crypto import CryptoEnv
from traintest import train

TICKER_LIST = ['BTCUSDT','ETHUSDT','ADAUSDT','BNBUSDT','XRPUSDT',
'SOLUSDT','DOTUSDT', 'DOGEUSDT','AVAXUSDT','UNIUSDT']
env = CryptoEnv
TRAIN_START_DATE = '2021-09-01'
TRAIN_END_DATE = '2021-09-02'

TEST_START_DATE = '2021-09-21'
TEST_END_DATE = '2021-09-30'

INDICATORS = ['macd','rsi']#self-defined technical indicator list is NOT supported yet

ERL_PARAMS = {"learning_rate": 2**-15,"batch_size": 211,
"gamma": 0.99, "seed":312,"net_dimension": 29,
"target_step": 5000, "eval_gap": 30, "eval_times": 1}

train(start_date=TRAIN_START_DATE,
end_date=TRAIN_END_DATE,
ticker_list=TICKER_LIST,
data_source='binance',
time_interval='1h',
technical_indicator_list=INDICATORS,
drl_lib='elegantrl',
env=env,
model_name='ppo',
current_working_dir='./test_ppo',
erl_params=ERL_PARAMS,
break_step=5e4,
if_vix=False
)

`
traintest.py

`from future import annotations
from finrl.config import ERL_PARAMS
from finrl.config import INDICATORS
from finrl.config import RLlib_PARAMS
from finrl.config import SAC_PARAMS
from finrl.config import TRAIN_END_DATE
from finrl.config import TRAIN_START_DATE
from finrl.config_tickers import DOW_30_TICKER
from meta.data_processor import DataProcessor
from finrl.meta.env_stock_trading.env_stocktrading_np import StockTradingEnv

def train(
start_date,
end_date,
ticker_list,
data_source,
time_interval,
technical_indicator_list,
drl_lib,
env,
model_name,
if_vix=True,
**kwargs,
):
# download data
dp = DataProcessor(data_source, start_date=start_date, end_date=end_date, time_interval=time_interval)
data = dp.download_data(ticker_list=ticker_list)
data = dp.clean_data()
df=dp.dataframe
df.head()
df.to_csv('binancedataYS.csv')
data = dp.add_technical_indicator(tech_indicator_list=technical_indicator_list)
df2=dp.dataframe
df2.head()
df2.to_csv('binancedataIND.csv')
if if_vix:
data = dp.add_vix(data)
price_array, tech_array, turbulence_array = dp.df_to_array(if_vix)
env_config = {
"price_array": price_array,
"tech_array": tech_array,
"turbulence_array": turbulence_array,
"if_train": True,
}
env_instance = env(config=env_config)
# read parameters
cwd = kwargs.get("cwd", "./" + str(model_name))
if drl_lib == "elegantrl":
from finrl.agents.elegantrl.models import DRLAgent as DRLAgent_erl
break_step = kwargs.get("break_step", 1e6)
erl_params = kwargs.get("erl_params")
agent = DRLAgent_erl(
env=env,
price_array=price_array,
tech_array=tech_array,
turbulence_array=turbulence_array,
)
model = agent.get_model(model_name, model_kwargs=erl_params)
trained_model = agent.train_model(
model=model, cwd=cwd, total_timesteps=break_step
)
elif drl_lib == "rllib":
total_episodes = kwargs.get("total_episodes", 100)
rllib_params = kwargs.get("rllib_params")
from finrl.agents.rllib.models import DRLAgent as DRLAgent_rllib
agent_rllib = DRLAgent_rllib(
env=env,
price_array=price_array,
tech_array=tech_array,
turbulence_array=turbulence_array,
)
model, model_config = agent_rllib.get_model(model_name)
model_config["lr"] = rllib_params["lr"]
model_config["train_batch_size"] = rllib_params["train_batch_size"]
model_config["gamma"] = rllib_params["gamma"]
# ray.shutdown()
trained_model = agent_rllib.train_model(
model=model,
model_name=model_name,
model_config=model_config,
total_episodes=total_episodes,
)
trained_model.save(cwd)
elif drl_lib == "stable_baselines3":
total_timesteps = kwargs.get("total_timesteps", 1e6)
agent_params = kwargs.get("agent_params")
from finrl.agents.stablebaselines3.models import DRLAgent as DRLAgent_sb3
agent = DRLAgent_sb3(env=env_instance)
model = agent.get_model(model_name, model_kwargs=agent_params)
trained_model = agent.train_model(
model=model, tb_log_name=model_name, total_timesteps=total_timesteps
)
print("Training is finished!")
trained_model.save(cwd)
print("Trained model is saved in " + str(cwd))
else:
raise ValueError("DRL library input is NOT supported. Please check.")
`