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Add proper handling of multiple environments

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This commit is contained in:
Richard Jozsa 2023-03-20 15:54:58 +01:00
parent d03fe1f8ee
commit 66c326b789
2 changed files with 14 additions and 11 deletions
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4
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
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@ -433,7 +433,6 @@ class BaseReinforcementLearningModel(IFreqaiModel):
def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
seed: int, train_df: DataFrame, price: DataFrame,
monitor: bool = False,
env_info: Dict[str, Any] = {}) -> Callable:
"""
Utility function for multiprocessed env.
@ -450,8 +449,7 @@ def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
env = MyRLEnv(df=train_df, prices=price, id=env_id, seed=seed + rank,
**env_info)
if monitor:
env = Monitor(env)
return env
set_random_seed(seed)
return _init

21
freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
View File

@ -4,7 +4,7 @@ from typing import Any, Dict
from pandas import DataFrame
from stable_baselines3.common.callbacks import EvalCallback
from stable_baselines3.common.vec_env import SubprocVecEnv
from stable_baselines3.common.vec_env import VecMonitor
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
from freqtrade.freqai.RL.BaseReinforcementLearningModel import make_env
@ -41,22 +41,27 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
env_info = self.pack_env_dict(dk.pair)
eval_freq = len(train_df) // self.max_threads
env_id = "train_env"
self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,
self.train_env = VecMonitor(SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1,
train_df, prices_train,
monitor=True,
env_info=env_info) for i
in range(self.max_threads)])
in range(self.max_threads)]))
eval_env_id = 'eval_env'
self.eval_env = SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
self.eval_env = VecMonitor(SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
test_df, prices_test,
monitor=True,
env_info=env_info) for i
in range(self.max_threads)])
in range(self.max_threads)]))
self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
render=False, eval_freq=len(train_df),
render=False, eval_freq=eval_freq,
best_model_save_path=str(dk.data_path))
# TENSORBOARD CALLBACK DOES NOT RECOMMENDED TO USE WITH MULTIPLE ENVS, IT WILL RETURN FALSE INFORMATIONS, NEVERTHLESS NOT THREAD SAFE WITH SB3!!!
actions = self.train_env.env_method("get_actions")[0]
self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)