From cef9642a6fa103fd7fbab0a82e0eb7cdbc2df765 Mon Sep 17 00:00:00 2001 From: Antonin RAFFIN Date: Thu, 16 Jul 2020 16:45:43 +0200 Subject: [PATCH] Remove unused imports --- stable_baselines3/common/off_policy_algorithm.py | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) diff --git a/stable_baselines3/common/off_policy_algorithm.py b/stable_baselines3/common/off_policy_algorithm.py index e31558e..35af80c 100644 --- a/stable_baselines3/common/off_policy_algorithm.py +++ b/stable_baselines3/common/off_policy_algorithm.py @@ -2,7 +2,7 @@ import io import pathlib import time import warnings -from typing import Any, Callable, Dict, Generic, List, Mapping, Optional, Tuple, Type, TypeVar, Union +from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Type, Union import gym import numpy as np @@ -148,7 +148,7 @@ class OffPolicyAlgorithm(BaseAlgorithm): self.use_sde_at_warmup = use_sde_at_warmup self.replay_buffer = None - self.replay_buffer_cls = replay_buffer_cls if replay_buffer_cls is not None else ReplayBuffer + self.replay_buffer_cls = replay_buffer_cls or ReplayBuffer self.replay_buffer_kwargs = dict(replay_buffer_kwargs or {}) def _setup_model(self) -> None: @@ -225,7 +225,7 @@ class OffPolicyAlgorithm(BaseAlgorithm): self.replay_buffer.dones[pos] = True return super()._setup_learn( - total_timesteps, eval_env, callback, eval_freq, n_eval_episodes, log_path, reset_num_timesteps, tb_log_name, + total_timesteps, eval_env, callback, eval_freq, n_eval_episodes, log_path, reset_num_timesteps, tb_log_name ) def learn( @@ -242,7 +242,7 @@ class OffPolicyAlgorithm(BaseAlgorithm): ) -> "OffPolicyAlgorithm": total_timesteps, callback = self._setup_learn( - total_timesteps, eval_env, callback, eval_freq, n_eval_episodes, eval_log_path, reset_num_timesteps, tb_log_name, + total_timesteps, eval_env, callback, eval_freq, n_eval_episodes, eval_log_path, reset_num_timesteps, tb_log_name ) callback.on_training_start(locals(), globals()) @@ -426,11 +426,7 @@ class OffPolicyAlgorithm(BaseAlgorithm): reward_ = self._vec_normalize_env.get_original_reward() else: # Avoid changing the original ones - self._last_original_obs, new_obs_, reward_ = ( - self._last_obs, - new_obs, - reward, - ) + self._last_original_obs, new_obs_, reward_ = self._last_obs, new_obs, reward replay_buffer.add(self._last_original_obs, new_obs_, buffer_action, reward_, done)