adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

persist a single training environment.

Browse Source
This commit is contained in:
robcaulk
2022-08-18 16:07:19 +02:00
parent 5d4e5e69fe
commit f95602f6bd
6 changed files with 162 additions and 129 deletions
Download Patch File Download Diff File Expand all files Collapse all files

49
freqtrade/freqai/prediction_models/ReinforcementLearningPPO_multiproc.py
View File

@@ -3,9 +3,7 @@ from typing import Any, Dict # , Tuple
import numpy as np
# import numpy.typing as npt
# import pandas as pd
import torch as th
# from pandas import DataFrame
from stable_baselines3.common.monitor import Monitor
from typing import Callable
from stable_baselines3 import PPO
@@ -16,7 +14,6 @@ from freqtrade.freqai.RL.Base3ActionRLEnv import Base3ActionRLEnv, Actions, Posi
from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
import gym
from pandas import DataFrame
logger = logging.getLogger(__name__)
@@ -48,26 +45,15 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
User created Reinforcement Learning Model prediction model.
"""
def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen,
prices_train: DataFrame, prices_test: DataFrame):
def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
eval_freq = self.freqai_info["rl_config"]["eval_cycles"] * len(test_df)
total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
env_id = "train_env"
num_cpu = int(dk.thread_count / 2)
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'
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)])
path = dk.data_path
eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
eval_callback = EvalCallback(self.eval_env, best_model_save_path=f"{path}/",
log_path=f"{path}/ppo/logs/", eval_freq=int(eval_freq),
deterministic=True, render=False)
@@ -75,7 +61,7 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
policy_kwargs = dict(activation_fn=th.nn.ReLU,
net_arch=[512, 512, 512])
model = PPO('MlpPolicy', train_env, policy_kwargs=policy_kwargs,
model = PPO('MlpPolicy', self.train_env, policy_kwargs=policy_kwargs,
tensorboard_log=f"{path}/ppo/tensorboard/",
**self.freqai_info['model_training_parameters']
)
@@ -87,10 +73,37 @@ class ReinforcementLearningPPO_multiproc(BaseReinforcementLearningModel):
best_model = PPO.load(dk.data_path / "best_model")
print('Training finished!')
eval_env.close()
return best_model
def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test):
"""
User overrides this in their prediction model if they are custom a MyRLEnv. Othwerwise
leaving this will default to Base5ActEnv
"""
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
# 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)])
else:
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.train_env.env_method('reset')
self.eval_env.env_method('reset')
class MyRLEnv(Base3ActionRLEnv):
"""

41
freqtrade/freqai/prediction_models/ReinforcementLearningTDQN.py
View File

@@ -9,8 +9,7 @@ from freqtrade.freqai.RL.TDQNagent import TDQN
from stable_baselines3 import DQN
from stable_baselines3.common.buffers import ReplayBuffer
import numpy as np
from pandas import DataFrame
import gc
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
@@ -21,24 +20,15 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
User created Reinforcement Learning Model prediction model.
"""
def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen,
prices_train: DataFrame, prices_test: DataFrame):
def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
eval_freq = self.freqai_info["rl_config"]["eval_cycles"] * len(test_df)
total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
# environments
train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params)
eval = MyRLEnv(df=test_df, prices=prices_test,
window_size=self.CONV_WIDTH, reward_kwargs=self.reward_params)
eval_env = Monitor(eval, ".")
eval_env.reset()
path = dk.data_path
eval_callback = EvalCallback(eval_env, best_model_save_path=f"{path}/",
eval_callback = EvalCallback(self.eval_env, best_model_save_path=f"{path}/",
log_path=f"{path}/tdqn/logs/", eval_freq=int(eval_freq),
deterministic=True, render=False)
@@ -46,7 +36,7 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
policy_kwargs = dict(activation_fn=th.nn.ReLU,
net_arch=[256, 256, 128])
model = TDQN('TMultiInputPolicy', train_env,
model = TDQN('TMultiInputPolicy', self.train_env,
tensorboard_log=f"{path}/tdqn/tensorboard/",
policy_kwargs=policy_kwargs,
replay_buffer_class=ReplayBuffer,
@@ -58,12 +48,33 @@ class ReinforcementLearningTDQN(BaseReinforcementLearningModel):
callback=eval_callback
)
del model
best_model = DQN.load(dk.data_path / "best_model")
print('Training finished!')
gc.collect()
return best_model
def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test):
"""
User overrides this as shown here if they are using a custom MyRLEnv
"""
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
# environments
if not self.train_env:
self.train_env = MyRLEnv(df=train_df, prices=prices_train, window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params)
self.eval_env = Monitor(MyRLEnv(df=test_df, prices=prices_test,
window_size=self.CONV_WIDTH,
reward_kwargs=self.reward_params), ".")
else:
self.train_env.reset_env(train_df, prices_train, self.CONV_WIDTH, self.reward_params)
self.eval_env.reset_env(train_df, prices_train, self.CONV_WIDTH, self.reward_params)
self.train_env.reset()
self.eval_env.reset()
# User can inherit and customize 5 action environment
class MyRLEnv(Base5ActionRLEnv):

70
freqtrade/freqai/prediction_models/ReinforcementLearningTDQN_multiproc.py
View File

@@ -4,8 +4,8 @@ import torch as th
import numpy as np
import gym
from typing import Callable
from stable_baselines3.common.callbacks import (
EvalCallback, StopTrainingOnNoModelImprovement, StopTrainingOnRewardThreshold)
from stable_baselines3.common.callbacks import EvalCallback
# EvalCallback , StopTrainingOnNoModelImprovement, StopTrainingOnRewardThreshold
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.vec_env import SubprocVecEnv
from stable_baselines3.common.utils import set_random_seed
@@ -15,7 +15,6 @@ from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcement
from freqtrade.freqai.RL.TDQNagent import TDQN
from stable_baselines3.common.buffers import ReplayBuffer
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from pandas import DataFrame
logger = logging.getLogger(__name__)
@@ -47,46 +46,23 @@ class ReinforcementLearningTDQN_multiproc(BaseReinforcementLearningModel):
User created Reinforcement Learning Model prediction model.
"""
def fit_rl(self, data_dictionary: Dict[str, Any], pair: str, dk: FreqaiDataKitchen,
prices_train: DataFrame, prices_test: DataFrame):
def fit_rl(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen):
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
eval_freq = self.freqai_info["rl_config"]["eval_cycles"] * len(test_df)
total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
env_id = "train_env"
num_cpu = int(dk.thread_count / 2)
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'
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)])
path = dk.data_path
stop_train_callback = StopTrainingOnNoModelImprovement(
max_no_improvement_evals=5,
min_evals=10,
verbose=2
)
callback_on_best = StopTrainingOnRewardThreshold(reward_threshold=-200, verbose=2)
eval_callback = EvalCallback(
eval_env, best_model_save_path=f"{path}/",
log_path=f"{path}/tdqn/logs/",
eval_freq=int(eval_freq),
deterministic=True,
render=True,
callback_after_eval=stop_train_callback,
callback_on_new_best=callback_on_best,
verbose=2
)
eval_callback = EvalCallback(self.eval_env, best_model_save_path=f"{path}/",
log_path=f"{path}/tdqn/logs/", eval_freq=int(eval_freq),
deterministic=True, render=False)
# model arch
policy_kwargs = dict(activation_fn=th.nn.ReLU,
net_arch=[512, 512, 512])
model = TDQN('TMultiInputPolicy', train_env,
model = TDQN('TMultiInputPolicy', self.train_env,
policy_kwargs=policy_kwargs,
tensorboard_log=f"{path}/tdqn/tensorboard/",
replay_buffer_class=ReplayBuffer,
@@ -100,12 +76,40 @@ class ReinforcementLearningTDQN_multiproc(BaseReinforcementLearningModel):
best_model = DQN.load(dk.data_path / "best_model.zip")
print('Training finished!')
eval_env.close()
return best_model
def set_train_and_eval_environments(self, data_dictionary, prices_train, prices_test):
"""
User overrides this in their prediction model if they are custom a MyRLEnv. Othwerwise
leaving this will default to Base5ActEnv
"""
train_df = data_dictionary["train_features"]
test_df = data_dictionary["test_features"]
# 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)])
else:
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.train_env.env_method('reset')
self.eval_env.env_method('reset')
# User can inherit and customize 5 action environment
class MyRLEnv(Base5ActionRLEnv):
"""
User can override any function in BaseRLEnv and gym.Env. Here the user