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fix persist a single training environment for PPO

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This commit is contained in:
sonnhfit
2022-08-19 01:49:11 +07:00
committed by robcaulk
parent f95602f6bd
commit 4baa36bdcf
3 changed files with 51 additions and 25 deletions
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45
freqtrade/freqai/prediction_models/ReinforcementLearningPPO.py
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@@ -1,16 +1,17 @@
import gc
import logging
from typing import Any, Dict # , Tuple
import numpy as np
# import numpy.typing as npt
import torch as th
from pandas import DataFrame
from stable_baselines3 import PPO
from stable_baselines3.common.callbacks import EvalCallback
from stable_baselines3.common.monitor import Monitor
from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Positions
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.RL.Base3ActionRLEnv import Actions, Base3ActionRLEnv, Positions
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
logger = logging.getLogger(__name__)
@@ -21,23 +22,15 @@ class ReinforcementLearningPPO(BaseReinforcementLearningModel):
User created Reinforcement Learning Model prediction model.
"""
def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen,
prices_train: DataFrame, prices_test: DataFrame):
def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
eval_freq = self.freqai_info["rl_config"]["eval_cycles"] * len(test_df)
total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
# environments
train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params)
eval = MyRLEnv(df=test_df, prices=prices_test,
window_size=self.CONV_WIDTH, reward_kwargs=self.reward_params)
eval_env = Monitor(eval, ".")
path = dk.data_path
eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
eval_callback = EvalCallback(self.eval_env, best_model_save_path=f"{path}/",
log_path=f"{path}/ppo/logs/", eval_freq=int(eval_freq),
deterministic=True, render=False)
@@ -45,8 +38,8 @@ class ReinforcementLearningPPO(BaseReinforcementLearningModel):
policy_kwargs = dict(activation_fn=th.nn.ReLU,
net_arch=[256, 256, 128])
model = PPO('MlpPolicy', train_env, policy_kwargs=policy_kwargs,
tensorboard_log=f"{path}/ppo/tensorboard/", learning_rate=0.00025,
model = PPO('MlpPolicy', self.train_env, policy_kwargs=policy_kwargs,
tensorboard_log=f"{path}/ppo/tensorboard/",
**self.freqai_info['model_training_parameters']
)
@@ -55,12 +48,34 @@ class ReinforcementLearningPPO(BaseReinforcementLearningModel):
callback=eval_callback
)
del model
best_model = PPO.load(dk.data_path / "best_model")
print('Training finished!')
gc.collect()
return best_model
def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test):
"""
User overrides this as shown here if they are using a custom MyRLEnv
"""
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
# environments
if not self.train_env:
self.train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params)
self.eval_env = Monitor(MyRLEnv(df=test_df, prices=prices_test,
window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params), ".")
else:
self.train_env.reset_env(train_df, prices_train, self.CONV_WIDTH, self.reward_params)
self.eval_env.reset_env(train_df, prices_train, self.CONV_WIDTH, self.reward_params)
self.train_env.reset()
self.eval_env.reset()
class MyRLEnv(Base3ActionRLEnv):
"""