263 lines
9.7 KiB
Python
263 lines
9.7 KiB
Python
# import logging
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# from pathlib import Path
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# from typing import Any, Dict, List, Optional, Tuple, Type, Union
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# import gym
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# import torch as th
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# from stable_baselines3 import DQN
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# from stable_baselines3.common.buffers import ReplayBuffer
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# from stable_baselines3.common.policies import BasePolicy
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# from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor
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# from stable_baselines3.common.type_aliases import GymEnv, Schedule
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# from stable_baselines3.dqn.policies import CnnPolicy, DQNPolicy, MlpPolicy, QNetwork
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# from torch import nn
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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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# logger = logging.getLogger(__name__)
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# class ReinforcementLearnerCustomAgent(BaseReinforcementLearningModel):
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# """
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# User can customize agent by defining the class and using it directly.
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# Here the example is "TDQN"
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# Warning!
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# This is an advanced example of how a user may create and use a highly
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# customized model class (which can inherit from existing classes,
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# similar to how the example below inherits from DQN).
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# This file is for example purposes only, and should not be run.
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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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# policy_kwargs = dict(activation_fn=th.nn.ReLU,
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# net_arch=[256, 256, 128])
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# # TDQN is a custom agent defined below
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# model = TDQN(self.policy_type, self.train_env,
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# tensorboard_log=str(Path(dk.data_path / "tensorboard")),
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# policy_kwargs=policy_kwargs,
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# **self.freqai_info['model_training_parameters']
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# )
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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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# # User creates their custom agent and networks as shown below
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# def create_mlp_(
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# input_dim: int,
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# output_dim: int,
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# net_arch: List[int],
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# activation_fn: Type[nn.Module] = nn.ReLU,
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# squash_output: bool = False,
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# ) -> List[nn.Module]:
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# dropout = 0.2
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# if len(net_arch) > 0:
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# number_of_neural = net_arch[0]
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# modules = [
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# nn.Linear(input_dim, number_of_neural),
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# nn.BatchNorm1d(number_of_neural),
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# nn.LeakyReLU(),
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# nn.Dropout(dropout),
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# nn.Linear(number_of_neural, number_of_neural),
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# nn.BatchNorm1d(number_of_neural),
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# nn.LeakyReLU(),
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# nn.Dropout(dropout),
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# nn.Linear(number_of_neural, number_of_neural),
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# nn.BatchNorm1d(number_of_neural),
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# nn.LeakyReLU(),
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# nn.Dropout(dropout),
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# nn.Linear(number_of_neural, number_of_neural),
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# nn.BatchNorm1d(number_of_neural),
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# nn.LeakyReLU(),
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# nn.Dropout(dropout),
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# nn.Linear(number_of_neural, output_dim)
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# ]
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# return modules
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# class TDQNetwork(QNetwork):
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# def __init__(self,
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# observation_space: gym.spaces.Space,
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# action_space: gym.spaces.Space,
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# features_extractor: nn.Module,
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# features_dim: int,
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# net_arch: Optional[List[int]] = None,
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# activation_fn: Type[nn.Module] = nn.ReLU,
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# normalize_images: bool = True
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# ):
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# super().__init__(
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# observation_space=observation_space,
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# action_space=action_space,
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# features_extractor=features_extractor,
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# features_dim=features_dim,
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# net_arch=net_arch,
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# activation_fn=activation_fn,
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# normalize_images=normalize_images
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# )
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# action_dim = self.action_space.n
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# q_net = create_mlp_(self.features_dim, action_dim, self.net_arch, self.activation_fn)
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# self.q_net = nn.Sequential(*q_net).apply(self.init_weights)
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# def init_weights(self, m):
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# if type(m) == nn.Linear:
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# th.nn.init.kaiming_uniform_(m.weight)
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# class TDQNPolicy(DQNPolicy):
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# def __init__(
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# self,
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# observation_space: gym.spaces.Space,
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# action_space: gym.spaces.Space,
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# lr_schedule: Schedule,
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# net_arch: Optional[List[int]] = None,
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# activation_fn: Type[nn.Module] = nn.ReLU,
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# features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
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# features_extractor_kwargs: Optional[Dict[str, Any]] = None,
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# normalize_images: bool = True,
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# optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
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# optimizer_kwargs: Optional[Dict[str, Any]] = None,
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# ):
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# super().__init__(
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# observation_space=observation_space,
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# action_space=action_space,
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# lr_schedule=lr_schedule,
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# net_arch=net_arch,
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# activation_fn=activation_fn,
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# features_extractor_class=features_extractor_class,
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# features_extractor_kwargs=features_extractor_kwargs,
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# normalize_images=normalize_images,
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# optimizer_class=optimizer_class,
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# optimizer_kwargs=optimizer_kwargs
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# )
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# @staticmethod
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# def init_weights(module: nn.Module, gain: float = 1) -> None:
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# """
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# Orthogonal initialization (used in PPO and A2C)
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# """
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# if isinstance(module, (nn.Linear, nn.Conv2d)):
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# nn.init.kaiming_uniform_(module.weight)
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# if module.bias is not None:
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# module.bias.data.fill_(0.0)
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# def make_q_net(self) -> TDQNetwork:
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# # Make sure we always have separate networks for features extractors etc
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# net_args = self._update_features_extractor(self.net_args, features_extractor=None)
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# return TDQNetwork(**net_args).to(self.device)
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# class TMultiInputPolicy(TDQNPolicy):
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# def __init__(
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# self,
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# observation_space: gym.spaces.Space,
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# action_space: gym.spaces.Space,
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# lr_schedule: Schedule,
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# net_arch: Optional[List[int]] = None,
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# activation_fn: Type[nn.Module] = nn.ReLU,
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# features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
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# features_extractor_kwargs: Optional[Dict[str, Any]] = None,
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# normalize_images: bool = True,
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# optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
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# optimizer_kwargs: Optional[Dict[str, Any]] = None,
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# ):
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# super().__init__(
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# observation_space,
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# action_space,
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# lr_schedule,
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# net_arch,
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# activation_fn,
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# features_extractor_class,
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# features_extractor_kwargs,
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# normalize_images,
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# optimizer_class,
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# optimizer_kwargs,
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# )
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# class TDQN(DQN):
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# policy_aliases: Dict[str, Type[BasePolicy]] = {
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# "MlpPolicy": MlpPolicy,
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# "CnnPolicy": CnnPolicy,
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# "TMultiInputPolicy": TMultiInputPolicy,
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# }
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# def __init__(
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# self,
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# policy: Union[str, Type[TDQNPolicy]],
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# env: Union[GymEnv, str],
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# learning_rate: Union[float, Schedule] = 1e-4,
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# buffer_size: int = 1000000, # 1e6
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# learning_starts: int = 50000,
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# batch_size: int = 32,
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# tau: float = 1.0,
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# gamma: float = 0.99,
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# train_freq: Union[int, Tuple[int, str]] = 4,
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# gradient_steps: int = 1,
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# replay_buffer_class: Optional[ReplayBuffer] = None,
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# replay_buffer_kwargs: Optional[Dict[str, Any]] = None,
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# optimize_memory_usage: bool = False,
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# target_update_interval: int = 10000,
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# exploration_fraction: float = 0.1,
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# exploration_initial_eps: float = 1.0,
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# exploration_final_eps: float = 0.05,
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# max_grad_norm: float = 10,
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# tensorboard_log: Optional[str] = None,
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# create_eval_env: bool = False,
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# policy_kwargs: Optional[Dict[str, Any]] = None,
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# verbose: int = 1,
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# seed: Optional[int] = None,
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# device: Union[th.device, str] = "auto",
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# _init_setup_model: bool = True,
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# ):
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# super().__init__(
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# policy=policy,
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# env=env,
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# learning_rate=learning_rate,
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# buffer_size=buffer_size,
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# learning_starts=learning_starts,
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# batch_size=batch_size,
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# tau=tau,
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# gamma=gamma,
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# train_freq=train_freq,
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# gradient_steps=gradient_steps,
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# replay_buffer_class=replay_buffer_class, # No action noise
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# replay_buffer_kwargs=replay_buffer_kwargs,
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# optimize_memory_usage=optimize_memory_usage,
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# target_update_interval=target_update_interval,
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# exploration_fraction=exploration_fraction,
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# exploration_initial_eps=exploration_initial_eps,
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# exploration_final_eps=exploration_final_eps,
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# max_grad_norm=max_grad_norm,
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# tensorboard_log=tensorboard_log,
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# create_eval_env=create_eval_env,
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# policy_kwargs=policy_kwargs,
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# verbose=verbose,
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# seed=seed,
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# device=device,
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# _init_setup_model=_init_setup_model
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# )
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