stable/freqtrade/freqai/RL/TensorboardCallback.py

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from enum import Enum
from typing import Any, Dict, Type, Union
from stable_baselines3.common.callbacks import BaseCallback
from stable_baselines3.common.logger import HParam
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from freqtrade.freqai.RL.BaseEnvironment import BaseActions, BaseEnvironment
class TensorboardCallback(BaseCallback):
"""
Custom callback for plotting additional values in tensorboard and
episodic summary reports.
"""
def __init__(self, verbose=1, actions: Type[Enum] = BaseActions):
super(TensorboardCallback, self).__init__(verbose)
self.model: Any = None
self.logger = None # type: Any
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self.training_env: BaseEnvironment = None # type: ignore
self.actions: Type[Enum] = actions
def _on_training_start(self) -> None:
hparam_dict = {
"algorithm": self.model.__class__.__name__,
"learning_rate": self.model.learning_rate,
# "gamma": self.model.gamma,
# "gae_lambda": self.model.gae_lambda,
# "batch_size": self.model.batch_size,
# "n_steps": self.model.n_steps,
}
metric_dict: Dict[str, Union[float, int]] = {
"eval/mean_reward": 0,
"rollout/ep_rew_mean": 0,
"rollout/ep_len_mean": 0,
"train/value_loss": 0,
"train/explained_variance": 0,
}
self.logger.record(
"hparams",
HParam(hparam_dict, metric_dict),
exclude=("stdout", "log", "json", "csv"),
)
def _on_step(self) -> bool:
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local_info = self.locals["infos"][0]
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custom_info = self.training_env.get_attr("custom_info")[0]
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for info in local_info:
if info not in ["episode", "terminal_observation"]:
self.logger.record(f"_info/{info}", local_info[info])
for info in custom_info:
if info in [action.name for action in self.actions]:
self.logger.record(f"_actions/{info}", custom_info[info])
else:
self.logger.record(f"_custom/{info}", custom_info[info])
return True