From bb62b0fc5a9e81ac12b867b6295c4a37cc8dc17e Mon Sep 17 00:00:00 2001 From: Shane Date: Sun, 26 May 2024 20:21:16 +1000 Subject: [PATCH] Update ReinforcementLearner_DDPG_TD3.py Clean up set policy code. --- .../ReinforcementLearner_DDPG_TD3.py | 17 ++++------------- 1 file changed, 4 insertions(+), 13 deletions(-) diff --git a/freqtrade/freqai/prediction_models/ReinforcementLearner_DDPG_TD3.py b/freqtrade/freqai/prediction_models/ReinforcementLearner_DDPG_TD3.py index 3846aeaef..180ffec0d 100644 --- a/freqtrade/freqai/prediction_models/ReinforcementLearner_DDPG_TD3.py +++ b/freqtrade/freqai/prediction_models/ReinforcementLearner_DDPG_TD3.py @@ -83,19 +83,10 @@ class ReinforcementLearner_DDPG_TD3(BaseReinforcementLearningModel): model_params["learning_rate"] = linear_schedule(_lr) logger.info(f"Learning rate linear schedule enabled, initial value: {_lr}") - if any(model in self.freqai_info["rl_config"]["model_type"] for model in ["DDPG", "TD3"]): - model_params["policy_kwargs"] = dict( - #net_arch=self.net_arch, - net_arch=dict(qf=self.net_arch, pi=self.net_arch), - activation_fn=th.nn.ReLU, - optimizer_class=th.optim.Adam - ) - else: - model_params["policy_kwargs"] = dict( - net_arch=dict(vf=self.net_arch, pi=self.net_arch), - activation_fn=th.nn.ReLU, - optimizer_class=th.optim.Adam - ) + model_params["policy_kwargs"] = dict( + net_arch=dict(vf=self.net_arch, pi=self.net_arch), + activation_fn=th.nn.ReLU, + optimizer_class=th.optim.Adam return model_params