skip darwin in RL tests, remove example scripts, improve doc
This commit is contained in:
robcaulk
@@ -1,262 +1,262 @@
|
||||
import logging
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List, Optional, Tuple, Type, Union
|
||||
# import logging
|
||||
# from pathlib import Path
|
||||
# from typing import Any, Dict, List, Optional, Tuple, Type, Union
|
||||
|
||||
import gym
|
||||
import torch as th
|
||||
from stable_baselines3 import DQN
|
||||
from stable_baselines3.common.buffers import ReplayBuffer
|
||||
from stable_baselines3.common.policies import BasePolicy
|
||||
from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor
|
||||
from stable_baselines3.common.type_aliases import GymEnv, Schedule
|
||||
from stable_baselines3.dqn.policies import CnnPolicy, DQNPolicy, MlpPolicy, QNetwork
|
||||
from torch import nn
|
||||
# import gym
|
||||
# import torch as th
|
||||
# from stable_baselines3 import DQN
|
||||
# from stable_baselines3.common.buffers import ReplayBuffer
|
||||
# from stable_baselines3.common.policies import BasePolicy
|
||||
# from stable_baselines3.common.torch_layers import BaseFeaturesExtractor, FlattenExtractor
|
||||
# from stable_baselines3.common.type_aliases import GymEnv, Schedule
|
||||
# from stable_baselines3.dqn.policies import CnnPolicy, DQNPolicy, MlpPolicy, QNetwork
|
||||
# from torch import nn
|
||||
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
|
||||
# from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
# from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
# logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ReinforcementLearnerCustomAgent(BaseReinforcementLearningModel):
|
||||
"""
|
||||
User can customize agent by defining the class and using it directly.
|
||||
Here the example is "TDQN"
|
||||
# class ReinforcementLearnerCustomAgent(BaseReinforcementLearningModel):
|
||||
# """
|
||||
# User can customize agent by defining the class and using it directly.
|
||||
# Here the example is "TDQN"
|
||||
|
||||
Warning!
|
||||
This is an advanced example of how a user may create and use a highly
|
||||
customized model class (which can inherit from existing classes,
|
||||
similar to how the example below inherits from DQN).
|
||||
This file is for example purposes only, and should not be run.
|
||||
"""
|
||||
# Warning!
|
||||
# This is an advanced example of how a user may create and use a highly
|
||||
# customized model class (which can inherit from existing classes,
|
||||
# similar to how the example below inherits from DQN).
|
||||
# This file is for example purposes only, and should not be run.
|
||||
# """
|
||||
|
||||
def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
|
||||
# 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)
|
||||
# train_df = data_dictionary["train_features"]
|
||||
# total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
|
||||
|
||||
policy_kwargs = dict(activation_fn=th.nn.ReLU,
|
||||
net_arch=[256, 256, 128])
|
||||
# policy_kwargs = dict(activation_fn=th.nn.ReLU,
|
||||
# net_arch=[256, 256, 128])
|
||||
|
||||
# TDQN is a custom agent defined below
|
||||
model = TDQN(self.policy_type, self.train_env,
|
||||
tensorboard_log=str(Path(dk.data_path / "tensorboard")),
|
||||
policy_kwargs=policy_kwargs,
|
||||
**self.freqai_info['model_training_parameters']
|
||||
)
|
||||
# # TDQN is a custom agent defined below
|
||||
# model = TDQN(self.policy_type, self.train_env,
|
||||
# tensorboard_log=str(Path(dk.data_path / "tensorboard")),
|
||||
# policy_kwargs=policy_kwargs,
|
||||
# **self.freqai_info['model_training_parameters']
|
||||
# )
|
||||
|
||||
model.learn(
|
||||
total_timesteps=int(total_timesteps),
|
||||
callback=self.eval_callback
|
||||
)
|
||||
# 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
|
||||
# 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.')
|
||||
# logger.info('Couldnt find best model, using final model instead.')
|
||||
|
||||
return model
|
||||
# return model
|
||||
|
||||
# User creates their custom agent and networks as shown below
|
||||
# # User creates their custom agent and networks as shown below
|
||||
|
||||
|
||||
def create_mlp_(
|
||||
input_dim: int,
|
||||
output_dim: int,
|
||||
net_arch: List[int],
|
||||
activation_fn: Type[nn.Module] = nn.ReLU,
|
||||
squash_output: bool = False,
|
||||
) -> List[nn.Module]:
|
||||
dropout = 0.2
|
||||
if len(net_arch) > 0:
|
||||
number_of_neural = net_arch[0]
|
||||
# def create_mlp_(
|
||||
# input_dim: int,
|
||||
# output_dim: int,
|
||||
# net_arch: List[int],
|
||||
# activation_fn: Type[nn.Module] = nn.ReLU,
|
||||
# squash_output: bool = False,
|
||||
# ) -> List[nn.Module]:
|
||||
# dropout = 0.2
|
||||
# if len(net_arch) > 0:
|
||||
# number_of_neural = net_arch[0]
|
||||
|
||||
modules = [
|
||||
nn.Linear(input_dim, number_of_neural),
|
||||
nn.BatchNorm1d(number_of_neural),
|
||||
nn.LeakyReLU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Linear(number_of_neural, number_of_neural),
|
||||
nn.BatchNorm1d(number_of_neural),
|
||||
nn.LeakyReLU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Linear(number_of_neural, number_of_neural),
|
||||
nn.BatchNorm1d(number_of_neural),
|
||||
nn.LeakyReLU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Linear(number_of_neural, number_of_neural),
|
||||
nn.BatchNorm1d(number_of_neural),
|
||||
nn.LeakyReLU(),
|
||||
nn.Dropout(dropout),
|
||||
nn.Linear(number_of_neural, output_dim)
|
||||
]
|
||||
return modules
|
||||
# modules = [
|
||||
# nn.Linear(input_dim, number_of_neural),
|
||||
# nn.BatchNorm1d(number_of_neural),
|
||||
# nn.LeakyReLU(),
|
||||
# nn.Dropout(dropout),
|
||||
# nn.Linear(number_of_neural, number_of_neural),
|
||||
# nn.BatchNorm1d(number_of_neural),
|
||||
# nn.LeakyReLU(),
|
||||
# nn.Dropout(dropout),
|
||||
# nn.Linear(number_of_neural, number_of_neural),
|
||||
# nn.BatchNorm1d(number_of_neural),
|
||||
# nn.LeakyReLU(),
|
||||
# nn.Dropout(dropout),
|
||||
# nn.Linear(number_of_neural, number_of_neural),
|
||||
# nn.BatchNorm1d(number_of_neural),
|
||||
# nn.LeakyReLU(),
|
||||
# nn.Dropout(dropout),
|
||||
# nn.Linear(number_of_neural, output_dim)
|
||||
# ]
|
||||
# return modules
|
||||
|
||||
|
||||
class TDQNetwork(QNetwork):
|
||||
def __init__(self,
|
||||
observation_space: gym.spaces.Space,
|
||||
action_space: gym.spaces.Space,
|
||||
features_extractor: nn.Module,
|
||||
features_dim: int,
|
||||
net_arch: Optional[List[int]] = None,
|
||||
activation_fn: Type[nn.Module] = nn.ReLU,
|
||||
normalize_images: bool = True
|
||||
):
|
||||
super().__init__(
|
||||
observation_space=observation_space,
|
||||
action_space=action_space,
|
||||
features_extractor=features_extractor,
|
||||
features_dim=features_dim,
|
||||
net_arch=net_arch,
|
||||
activation_fn=activation_fn,
|
||||
normalize_images=normalize_images
|
||||
)
|
||||
action_dim = self.action_space.n
|
||||
q_net = create_mlp_(self.features_dim, action_dim, self.net_arch, self.activation_fn)
|
||||
self.q_net = nn.Sequential(*q_net).apply(self.init_weights)
|
||||
# class TDQNetwork(QNetwork):
|
||||
# def __init__(self,
|
||||
# observation_space: gym.spaces.Space,
|
||||
# action_space: gym.spaces.Space,
|
||||
# features_extractor: nn.Module,
|
||||
# features_dim: int,
|
||||
# net_arch: Optional[List[int]] = None,
|
||||
# activation_fn: Type[nn.Module] = nn.ReLU,
|
||||
# normalize_images: bool = True
|
||||
# ):
|
||||
# super().__init__(
|
||||
# observation_space=observation_space,
|
||||
# action_space=action_space,
|
||||
# features_extractor=features_extractor,
|
||||
# features_dim=features_dim,
|
||||
# net_arch=net_arch,
|
||||
# activation_fn=activation_fn,
|
||||
# normalize_images=normalize_images
|
||||
# )
|
||||
# action_dim = self.action_space.n
|
||||
# q_net = create_mlp_(self.features_dim, action_dim, self.net_arch, self.activation_fn)
|
||||
# self.q_net = nn.Sequential(*q_net).apply(self.init_weights)
|
||||
|
||||
def init_weights(self, m):
|
||||
if type(m) == nn.Linear:
|
||||
th.nn.init.kaiming_uniform_(m.weight)
|
||||
# def init_weights(self, m):
|
||||
# if type(m) == nn.Linear:
|
||||
# th.nn.init.kaiming_uniform_(m.weight)
|
||||
|
||||
|
||||
class TDQNPolicy(DQNPolicy):
|
||||
# class TDQNPolicy(DQNPolicy):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
observation_space: gym.spaces.Space,
|
||||
action_space: gym.spaces.Space,
|
||||
lr_schedule: Schedule,
|
||||
net_arch: Optional[List[int]] = None,
|
||||
activation_fn: Type[nn.Module] = nn.ReLU,
|
||||
features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
|
||||
features_extractor_kwargs: Optional[Dict[str, Any]] = None,
|
||||
normalize_images: bool = True,
|
||||
optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
|
||||
optimizer_kwargs: Optional[Dict[str, Any]] = None,
|
||||
):
|
||||
super().__init__(
|
||||
observation_space=observation_space,
|
||||
action_space=action_space,
|
||||
lr_schedule=lr_schedule,
|
||||
net_arch=net_arch,
|
||||
activation_fn=activation_fn,
|
||||
features_extractor_class=features_extractor_class,
|
||||
features_extractor_kwargs=features_extractor_kwargs,
|
||||
normalize_images=normalize_images,
|
||||
optimizer_class=optimizer_class,
|
||||
optimizer_kwargs=optimizer_kwargs
|
||||
)
|
||||
# def __init__(
|
||||
# self,
|
||||
# observation_space: gym.spaces.Space,
|
||||
# action_space: gym.spaces.Space,
|
||||
# lr_schedule: Schedule,
|
||||
# net_arch: Optional[List[int]] = None,
|
||||
# activation_fn: Type[nn.Module] = nn.ReLU,
|
||||
# features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
|
||||
# features_extractor_kwargs: Optional[Dict[str, Any]] = None,
|
||||
# normalize_images: bool = True,
|
||||
# optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
|
||||
# optimizer_kwargs: Optional[Dict[str, Any]] = None,
|
||||
# ):
|
||||
# super().__init__(
|
||||
# observation_space=observation_space,
|
||||
# action_space=action_space,
|
||||
# lr_schedule=lr_schedule,
|
||||
# net_arch=net_arch,
|
||||
# activation_fn=activation_fn,
|
||||
# features_extractor_class=features_extractor_class,
|
||||
# features_extractor_kwargs=features_extractor_kwargs,
|
||||
# normalize_images=normalize_images,
|
||||
# optimizer_class=optimizer_class,
|
||||
# optimizer_kwargs=optimizer_kwargs
|
||||
# )
|
||||
|
||||
@staticmethod
|
||||
def init_weights(module: nn.Module, gain: float = 1) -> None:
|
||||
"""
|
||||
Orthogonal initialization (used in PPO and A2C)
|
||||
"""
|
||||
if isinstance(module, (nn.Linear, nn.Conv2d)):
|
||||
nn.init.kaiming_uniform_(module.weight)
|
||||
if module.bias is not None:
|
||||
module.bias.data.fill_(0.0)
|
||||
# @staticmethod
|
||||
# def init_weights(module: nn.Module, gain: float = 1) -> None:
|
||||
# """
|
||||
# Orthogonal initialization (used in PPO and A2C)
|
||||
# """
|
||||
# if isinstance(module, (nn.Linear, nn.Conv2d)):
|
||||
# nn.init.kaiming_uniform_(module.weight)
|
||||
# if module.bias is not None:
|
||||
# module.bias.data.fill_(0.0)
|
||||
|
||||
def make_q_net(self) -> TDQNetwork:
|
||||
# Make sure we always have separate networks for features extractors etc
|
||||
net_args = self._update_features_extractor(self.net_args, features_extractor=None)
|
||||
return TDQNetwork(**net_args).to(self.device)
|
||||
# def make_q_net(self) -> TDQNetwork:
|
||||
# # Make sure we always have separate networks for features extractors etc
|
||||
# net_args = self._update_features_extractor(self.net_args, features_extractor=None)
|
||||
# return TDQNetwork(**net_args).to(self.device)
|
||||
|
||||
|
||||
class TMultiInputPolicy(TDQNPolicy):
|
||||
def __init__(
|
||||
self,
|
||||
observation_space: gym.spaces.Space,
|
||||
action_space: gym.spaces.Space,
|
||||
lr_schedule: Schedule,
|
||||
net_arch: Optional[List[int]] = None,
|
||||
activation_fn: Type[nn.Module] = nn.ReLU,
|
||||
features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
|
||||
features_extractor_kwargs: Optional[Dict[str, Any]] = None,
|
||||
normalize_images: bool = True,
|
||||
optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
|
||||
optimizer_kwargs: Optional[Dict[str, Any]] = None,
|
||||
):
|
||||
super().__init__(
|
||||
observation_space,
|
||||
action_space,
|
||||
lr_schedule,
|
||||
net_arch,
|
||||
activation_fn,
|
||||
features_extractor_class,
|
||||
features_extractor_kwargs,
|
||||
normalize_images,
|
||||
optimizer_class,
|
||||
optimizer_kwargs,
|
||||
)
|
||||
# class TMultiInputPolicy(TDQNPolicy):
|
||||
# def __init__(
|
||||
# self,
|
||||
# observation_space: gym.spaces.Space,
|
||||
# action_space: gym.spaces.Space,
|
||||
# lr_schedule: Schedule,
|
||||
# net_arch: Optional[List[int]] = None,
|
||||
# activation_fn: Type[nn.Module] = nn.ReLU,
|
||||
# features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
|
||||
# features_extractor_kwargs: Optional[Dict[str, Any]] = None,
|
||||
# normalize_images: bool = True,
|
||||
# optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
|
||||
# optimizer_kwargs: Optional[Dict[str, Any]] = None,
|
||||
# ):
|
||||
# super().__init__(
|
||||
# observation_space,
|
||||
# action_space,
|
||||
# lr_schedule,
|
||||
# net_arch,
|
||||
# activation_fn,
|
||||
# features_extractor_class,
|
||||
# features_extractor_kwargs,
|
||||
# normalize_images,
|
||||
# optimizer_class,
|
||||
# optimizer_kwargs,
|
||||
# )
|
||||
|
||||
|
||||
class TDQN(DQN):
|
||||
# class TDQN(DQN):
|
||||
|
||||
policy_aliases: Dict[str, Type[BasePolicy]] = {
|
||||
"MlpPolicy": MlpPolicy,
|
||||
"CnnPolicy": CnnPolicy,
|
||||
"TMultiInputPolicy": TMultiInputPolicy,
|
||||
}
|
||||
# policy_aliases: Dict[str, Type[BasePolicy]] = {
|
||||
# "MlpPolicy": MlpPolicy,
|
||||
# "CnnPolicy": CnnPolicy,
|
||||
# "TMultiInputPolicy": TMultiInputPolicy,
|
||||
# }
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
policy: Union[str, Type[TDQNPolicy]],
|
||||
env: Union[GymEnv, str],
|
||||
learning_rate: Union[float, Schedule] = 1e-4,
|
||||
buffer_size: int = 1000000, # 1e6
|
||||
learning_starts: int = 50000,
|
||||
batch_size: int = 32,
|
||||
tau: float = 1.0,
|
||||
gamma: float = 0.99,
|
||||
train_freq: Union[int, Tuple[int, str]] = 4,
|
||||
gradient_steps: int = 1,
|
||||
replay_buffer_class: Optional[ReplayBuffer] = None,
|
||||
replay_buffer_kwargs: Optional[Dict[str, Any]] = None,
|
||||
optimize_memory_usage: bool = False,
|
||||
target_update_interval: int = 10000,
|
||||
exploration_fraction: float = 0.1,
|
||||
exploration_initial_eps: float = 1.0,
|
||||
exploration_final_eps: float = 0.05,
|
||||
max_grad_norm: float = 10,
|
||||
tensorboard_log: Optional[str] = None,
|
||||
create_eval_env: bool = False,
|
||||
policy_kwargs: Optional[Dict[str, Any]] = None,
|
||||
verbose: int = 1,
|
||||
seed: Optional[int] = None,
|
||||
device: Union[th.device, str] = "auto",
|
||||
_init_setup_model: bool = True,
|
||||
):
|
||||
# def __init__(
|
||||
# self,
|
||||
# policy: Union[str, Type[TDQNPolicy]],
|
||||
# env: Union[GymEnv, str],
|
||||
# learning_rate: Union[float, Schedule] = 1e-4,
|
||||
# buffer_size: int = 1000000, # 1e6
|
||||
# learning_starts: int = 50000,
|
||||
# batch_size: int = 32,
|
||||
# tau: float = 1.0,
|
||||
# gamma: float = 0.99,
|
||||
# train_freq: Union[int, Tuple[int, str]] = 4,
|
||||
# gradient_steps: int = 1,
|
||||
# replay_buffer_class: Optional[ReplayBuffer] = None,
|
||||
# replay_buffer_kwargs: Optional[Dict[str, Any]] = None,
|
||||
# optimize_memory_usage: bool = False,
|
||||
# target_update_interval: int = 10000,
|
||||
# exploration_fraction: float = 0.1,
|
||||
# exploration_initial_eps: float = 1.0,
|
||||
# exploration_final_eps: float = 0.05,
|
||||
# max_grad_norm: float = 10,
|
||||
# tensorboard_log: Optional[str] = None,
|
||||
# create_eval_env: bool = False,
|
||||
# policy_kwargs: Optional[Dict[str, Any]] = None,
|
||||
# verbose: int = 1,
|
||||
# seed: Optional[int] = None,
|
||||
# device: Union[th.device, str] = "auto",
|
||||
# _init_setup_model: bool = True,
|
||||
# ):
|
||||
|
||||
super().__init__(
|
||||
policy=policy,
|
||||
env=env,
|
||||
learning_rate=learning_rate,
|
||||
buffer_size=buffer_size,
|
||||
learning_starts=learning_starts,
|
||||
batch_size=batch_size,
|
||||
tau=tau,
|
||||
gamma=gamma,
|
||||
train_freq=train_freq,
|
||||
gradient_steps=gradient_steps,
|
||||
replay_buffer_class=replay_buffer_class, # No action noise
|
||||
replay_buffer_kwargs=replay_buffer_kwargs,
|
||||
optimize_memory_usage=optimize_memory_usage,
|
||||
target_update_interval=target_update_interval,
|
||||
exploration_fraction=exploration_fraction,
|
||||
exploration_initial_eps=exploration_initial_eps,
|
||||
exploration_final_eps=exploration_final_eps,
|
||||
max_grad_norm=max_grad_norm,
|
||||
tensorboard_log=tensorboard_log,
|
||||
create_eval_env=create_eval_env,
|
||||
policy_kwargs=policy_kwargs,
|
||||
verbose=verbose,
|
||||
seed=seed,
|
||||
device=device,
|
||||
_init_setup_model=_init_setup_model
|
||||
)
|
||||
# super().__init__(
|
||||
# policy=policy,
|
||||
# env=env,
|
||||
# learning_rate=learning_rate,
|
||||
# buffer_size=buffer_size,
|
||||
# learning_starts=learning_starts,
|
||||
# batch_size=batch_size,
|
||||
# tau=tau,
|
||||
# gamma=gamma,
|
||||
# train_freq=train_freq,
|
||||
# gradient_steps=gradient_steps,
|
||||
# replay_buffer_class=replay_buffer_class, # No action noise
|
||||
# replay_buffer_kwargs=replay_buffer_kwargs,
|
||||
# optimize_memory_usage=optimize_memory_usage,
|
||||
# target_update_interval=target_update_interval,
|
||||
# exploration_fraction=exploration_fraction,
|
||||
# exploration_initial_eps=exploration_initial_eps,
|
||||
# exploration_final_eps=exploration_final_eps,
|
||||
# max_grad_norm=max_grad_norm,
|
||||
# tensorboard_log=tensorboard_log,
|
||||
# create_eval_env=create_eval_env,
|
||||
# policy_kwargs=policy_kwargs,
|
||||
# verbose=verbose,
|
||||
# seed=seed,
|
||||
# device=device,
|
||||
# _init_setup_model=_init_setup_model
|
||||
# )
|
||||
|
||||
Reference in New Issue
Block a user