linter fix

algorithms/minari/any_percent_bc.py

@@ -1,9 +1,9 @@
-from typing import Any, Dict, List, Optional, Tuple, Union
 import contextlib
-from dataclasses import asdict, dataclass
 import os
 import random
 import uuid
+from dataclasses import asdict, dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import gymnasium as gym
 import minari
@@ -12,9 +12,8 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from tqdm.auto import trange
-
 import wandb
+from tqdm.auto import trange
 
 TensorBatch = List[torch.Tensor]
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

algorithms/minari/iql.py

@@ -5,27 +5,26 @@
 # 1. iql_deterministic is true only for 2 datasets. Can we remote it?
 # 2. MLP class introduced bugs in the past. We should remove it.
 # 3. Refactor IQL updating code to be more consistent in style
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 import contextlib
 import copy
-from dataclasses import asdict, dataclass
 import os
 import random
 import uuid
+from dataclasses import asdict, dataclass
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import gymnasium as gym
 import minari
 import numpy as np
 import pyrallis
 import torch
-from torch.distributions import Normal
 import torch.nn as nn
 import torch.nn.functional as F
+import wandb
+from torch.distributions import Normal
 from torch.optim.lr_scheduler import CosineAnnealingLR
 from tqdm.auto import trange
 
-import wandb
-
 TensorBatch = List[torch.Tensor]
 
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"