import logging from typing import Any, Dict # , Tuple # import numpy.typing as npt import torch as th from stable_baselines3.common.callbacks import EvalCallback from stable_baselines3.common.vec_env import SubprocVecEnv from freqtrade.freqai.RL.BaseReinforcementLearningModel import (BaseReinforcementLearningModel, make_env) from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from pathlib import Path logger = logging.getLogger(__name__) class ReinforcementLearner_multiproc(BaseReinforcementLearningModel): """ User created Reinforcement Learning Model prediction model. """ def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen): train_df = data_dictionary["train_features"] total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df) # model arch policy_kwargs = dict(activation_fn=th.nn.ReLU, net_arch=[512, 512, 256]) model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs, tensorboard_log=Path(dk.full_path / "tensorboard"), **self.freqai_info['model_training_parameters'] ) model.learn( total_timesteps=int(total_timesteps), callback=self.eval_callback ) if Path(dk.data_path / "best_model.zip").is_file(): logger.info('Callback found a best model.') best_model = self.MODELCLASS.load(dk.data_path / "best_model") return best_model logger.info('Couldnt find best model, using final model instead.') return model def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test, dk): """ If user has particular environment configuration needs, they can do that by overriding this function. In the present case, the user wants to setup training environments for multiple workers. """ train_df = data_dictionary["train_features"] test_df = data_dictionary["test_features"] eval_freq = self.freqai_info["rl_config"]["eval_cycles"] * len(test_df) # environments if not self.train_env: env_id = "train_env" num_cpu = int(self.freqai_info["rl_config"]["thread_count"] / 2) self.train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, prices_train, self.reward_params, self.CONV_WIDTH, config=self.config) for i in range(num_cpu)]) eval_env_id = 'eval_env' self.eval_env = SubprocVecEnv([make_env(eval_env_id, i, 1, test_df, prices_test, self.reward_params, self.CONV_WIDTH, monitor=True, config=self.config) for i in range(num_cpu)]) self.eval_callback = EvalCallback(self.eval_env, deterministic=True, render=False, eval_freq=eval_freq, best_model_save_path=dk.data_path) else: self.train_env.env_method('reset') self.eval_env.env_method('reset') self.train_env.env_method('reset_env', train_df, prices_train, self.CONV_WIDTH, self.reward_params) self.eval_env.env_method('reset_env', train_df, prices_train, self.CONV_WIDTH, self.reward_params) self.eval_callback.__init__(self.eval_env, deterministic=True, render=False, eval_freq=eval_freq, best_model_save_path=dk.data_path)