83 lines
3.6 KiB
Python
83 lines
3.6 KiB
Python
import logging
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from pathlib import Path
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from typing import Any, Dict # , Tuple
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# import numpy.typing as npt
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import torch as th
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from stable_baselines3.common.callbacks import EvalCallback
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from stable_baselines3.common.vec_env import SubprocVecEnv
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.RL.BaseReinforcementLearningModel import (BaseReinforcementLearningModel,
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make_env)
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logger = logging.getLogger(__name__)
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class ReinforcementLearner_multiproc(BaseReinforcementLearningModel):
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"""
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User created Reinforcement Learning Model prediction model.
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"""
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def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
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train_df = data_dictionary["train_features"]
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total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
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# model arch
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policy_kwargs = dict(activation_fn=th.nn.ReLU,
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net_arch=[256, 256, 128])
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if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
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model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
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tensorboard_log=Path(dk.full_path / "tensorboard"),
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**self.freqai_info['model_training_parameters']
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)
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else:
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logger.info('Continual learning activated - starting training from previously '
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'trained agent.')
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model = self.dd.model_dictionary[dk.pair]
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model.tensorboard_log = Path(dk.data_path / "tensorboard")
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model.set_env(self.train_env)
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model.learn(
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total_timesteps=int(total_timesteps),
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callback=self.eval_callback
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)
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if Path(dk.data_path / "best_model.zip").is_file():
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logger.info('Callback found a best model.')
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best_model = self.MODELCLASS.load(dk.data_path / "best_model")
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return best_model
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logger.info('Couldnt find best model, using final model instead.')
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return model
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def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test, dk):
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"""
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If user has particular environment configuration needs, they can do that by
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overriding this function. In the present case, the user wants to setup training
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environments for multiple workers.
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"""
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train_df = data_dictionary["train_features"]
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test_df = data_dictionary["test_features"]
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env_id = "train_env"
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num_cpu = int(self.freqai_info["rl_config"]["thread_count"])
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self.train_env = SubprocVecEnv([make_env(self.MyRLEnv, env_id, i, 1, train_df, prices_train,
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self.reward_params, self.CONV_WIDTH, monitor=True,
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config=self.config) for i
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in range(num_cpu)])
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eval_env_id = 'eval_env'
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self.eval_env = SubprocVecEnv([make_env(self.MyRLEnv, eval_env_id, i, 1,
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test_df, prices_test,
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self.reward_params, self.CONV_WIDTH, monitor=True,
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config=self.config) for i
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in range(num_cpu)])
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self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
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render=False, eval_freq=len(train_df),
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best_model_save_path=dk.data_path)
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