reuse callback, allow user to acces all stable_baselines3 agents via config

freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py

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+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, 512])
+
+        model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
+                                tensorboard_log=Path(dk.data_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["data_kitchen_thread_count"] / 2)
+            self.train_env = SubprocVecEnv([make_env(env_id, i, 1, train_df, prices_train,
+                                            self.reward_params, self.CONV_WIDTH) 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) 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)