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add dp to multiproc

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This commit is contained in:
robcaulk 2022-12-14 18:22:20 +01:00
parent 350cebb0a8
commit 2285ca7d2a
2 changed files with 6 additions and 4 deletions
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6
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
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@ -24,6 +24,7 @@ from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv
from freqtrade.freqai.RL.BaseEnvironment import BaseActions, Positions
from freqtrade.freqai.RL.TensorboardCallback import TensorboardCallback
from freqtrade.persistence import Trade
from freqtrade.data.dataprovider import DataProvider
logger = logging.getLogger(__name__)
@ -384,7 +385,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
seed: int, train_df: DataFrame, price: DataFrame,
reward_params: Dict[str, int], window_size: int, monitor: bool = False,
config: Dict[str, Any] = {}) -> Callable:
config: Dict[str, Any] = {}, dp: DataProvider = None) -> Callable:
"""
Utility function for multiprocessed env.
@ -398,7 +399,8 @@ def make_env(MyRLEnv: Type[gym.Env], env_id: str, rank: int,
def _init() -> gym.Env:
env = MyRLEnv(df=train_df, prices=price, window_size=window_size,
reward_kwargs=reward_params, id=env_id, seed=seed + rank, config=config)
reward_kwargs=reward_params, id=env_id, seed=seed + rank,
config=config, dp=dp)
if monitor:
env = Monitor(env)
return env

4
freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
View File

@ -37,14 +37,14 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
env_id = "train_env"
self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1, train_df, prices_train,
self.reward_params, self.CONV_WIDTH, monitor=True,
config=self.config) for i
config=self.config, dp=self.data_provider) for i
in range(self.max_threads)])
eval_env_id = 'eval_env'
self.eval_env = SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
test_df, prices_test,
self.reward_params, self.CONV_WIDTH, monitor=True,
config=self.config) for i
config=self.config, dp=self.data_provider) for i
in range(self.max_threads)])
self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
render=False, eval_freq=len(train_df),