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fix monitor bug, set default values in case user doesnt set params

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This commit is contained in:
robcaulk 2022-08-24 16:32:14 +02:00
parent c0cee5df07
commit bd870e2331
3 changed files with 6 additions and 5 deletions
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4
freqtrade/freqai/RL/BaseReinforcementLearningModel.py
View File

@ -42,7 +42,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
self.eval_callback: EvalCallback = None
self.model_type = self.freqai_info['rl_config']['model_type']
self.rl_config = self.freqai_info['rl_config']
self.continual_retraining = self.rl_config['continual_retraining']
self.continual_retraining = self.rl_config.get('continual_retraining', False)
if self.model_type in SB3_MODELS:
import_str = 'stable_baselines3'
elif self.model_type in SB3_CONTRIB_MODELS:
@ -289,7 +289,7 @@ class MyRLEnv(Base5ActionRLEnv):
return 0.
pnl = self.get_unrealized_profit()
max_trade_duration = self.rl_config['max_trade_duration_candles']
max_trade_duration = self.rl_config.get('max_trade_duration_candles', 100)
trade_duration = self._current_tick - self._last_trade_tick
factor = 1

3
freqtrade/freqai/prediction_models/ReinforcementLearner.py
View File

@ -32,6 +32,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
logger.info('Continual training activated - starting training from previously '
'trained agent.')
model = self.dd.model_dictionary[dk.pair]
model.tensorboard_log = Path(dk.data_path / "tensorboard")
model.set_env(self.train_env)
model.learn(
@ -61,7 +62,7 @@ class MyRLEnv(Base5ActionRLEnv):
return 0.
pnl = self.get_unrealized_profit()
max_trade_duration = self.rl_config['max_trade_duration_candles']
max_trade_duration = self.rl_config.get('max_trade_duration_candles', 100)
trade_duration = self._current_tick - self._last_trade_tick
factor = 1

4
freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
View File

@ -26,10 +26,10 @@ class ReinforcementLearner_multiproc(BaseReinforcementLearningModel):
# model arch
policy_kwargs = dict(activation_fn=th.nn.ReLU,
net_arch=[512, 512, 512])
net_arch=[512, 512, 256])
model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
tensorboard_log=Path(dk.data_path / "tensorboard"),
tensorboard_log=Path(dk.full_path / "tensorboard"),
**self.freqai_info['model_training_parameters']
)