Switch to ruff

.github/workflows/format_check.yml

@@ -25,10 +25,7 @@ jobs:
           pip install Cython numpy
           pip install -e .
           pip install -r dev.requirements.txt
-      - name: Check format
-        run: |
-          ./scripts/format
-      - name: Linter
+      - name: Static analysis
         run: |
           ./scripts/lint
 

CONTRIBUTING.md

@@ -29,16 +29,10 @@ $ ./scripts/test
 ```
 
 ### Coding style check
-This repository is styled with [black](https://github.com/psf/black) formatter.
-Also, [isort](https://github.com/PyCQA/isort) is used to format package imports.
+This repository is styled and analyzed with [Ruff](https://docs.astral.sh/ruff/).
 [docformatter](https://github.com/PyCQA/docformatter) is additionally used to format docstrings.
-```
-$ ./scripts/format
-```
-
-### Linter
 This repository is fully type-annotated and checked by [mypy](https://github.com/python/mypy).
-Also, [pylint](https://github.com/PyCQA/pylint) checks code consistency.
+Before you submit your PR, please execute this command:
 ```
 $ ./scripts/lint
 ```

d3rlpy/algos/qlearning/base.py

@@ -2,13 +2,10 @@
 from collections import defaultdict
 from typing import (
     Callable,
-    Dict,
     Generator,
     Generic,
-    List,
     Optional,
     Sequence,
-    Tuple,
     TypeVar,
 )
 
@@ -67,13 +64,13 @@
 
 class QLearningAlgoImplBase(ImplBase):
     @train_api
-    def update(self, batch: TorchMiniBatch, grad_step: int) -> Dict[str, float]:
+    def update(self, batch: TorchMiniBatch, grad_step: int) -> dict[str, float]:
         return self.inner_update(batch, grad_step)
 
     @abstractmethod
     def inner_update(
         self, batch: TorchMiniBatch, grad_step: int
-    ) -> Dict[str, float]:
+    ) -> dict[str, float]:
         pass
 
     @eval_api
@@ -382,10 +379,10 @@ def fit(
         logger_adapter: LoggerAdapterFactory = FileAdapterFactory(),
         show_progress: bool = True,
         save_interval: int = 1,
-        evaluators: Optional[Dict[str, EvaluatorProtocol]] = None,
+        evaluators: Optional[dict[str, EvaluatorProtocol]] = None,
         callback: Optional[Callable[[Self, int, int], None]] = None,
         epoch_callback: Optional[Callable[[Self, int, int], None]] = None,
-    ) -> List[Tuple[int, Dict[str, float]]]:
+    ) -> list[tuple[int, dict[str, float]]]:
         """Trains with given dataset.
 
         .. code-block:: python
@@ -448,10 +445,10 @@ def fitter(
         logger_adapter: LoggerAdapterFactory = FileAdapterFactory(),
         show_progress: bool = True,
         save_interval: int = 1,
-        evaluators: Optional[Dict[str, EvaluatorProtocol]] = None,
+        evaluators: Optional[dict[str, EvaluatorProtocol]] = None,
         callback: Optional[Callable[[Self, int, int], None]] = None,
         epoch_callback: Optional[Callable[[Self, int, int], None]] = None,
-    ) -> Generator[Tuple[int, Dict[str, float]], None, None]:
+    ) -> Generator[tuple[int, dict[str, float]], None, None]:
         """Iterate over epochs steps to train with the given dataset. At each
         iteration algo methods and properties can be changed or queried.
 
@@ -859,7 +856,7 @@ def collect(
 
         return buffer
 
-    def update(self, batch: TransitionMiniBatch) -> Dict[str, float]:
+    def update(self, batch: TransitionMiniBatch) -> dict[str, float]:
         """Update parameters with mini-batch of data.
 
         Args:

d3rlpy/algos/qlearning/random_policy.py

@@ -1,5 +1,4 @@
 import dataclasses
-from typing import Dict
 
 import numpy as np
 
@@ -35,7 +34,9 @@ class RandomPolicyConfig(LearnableConfig):
     distribution: str = "uniform"
     normal_std: float = 1.0
 
-    def create(self, device: DeviceArg = False, enable_ddp: bool = False) -> "RandomPolicy":  # type: ignore
+    def create(  # type: ignore
+        self, device: DeviceArg = False, enable_ddp: bool = False
+    ) -> "RandomPolicy":
         return RandomPolicy(self)
 
     @staticmethod
@@ -83,7 +84,7 @@ def sample_action(self, x: Observation) -> NDArray:
     def predict_value(self, x: Observation, action: NDArray) -> NDArray:
         raise NotImplementedError
 
-    def inner_update(self, batch: TorchMiniBatch) -> Dict[str, float]:
+    def inner_update(self, batch: TorchMiniBatch) -> dict[str, float]:
         raise NotImplementedError
 
     def get_action_type(self) -> ActionSpace:
@@ -98,7 +99,9 @@ class DiscreteRandomPolicyConfig(LearnableConfig):
     ``fit`` and ``fit_online`` methods will raise exceptions.
     """
 
-    def create(self, device: DeviceArg = False, enable_ddp: bool = False) -> "DiscreteRandomPolicy":  # type: ignore
+    def create(  # type: ignore
+        self, device: DeviceArg = False, enable_ddp: bool = False
+    ) -> "DiscreteRandomPolicy":
         return DiscreteRandomPolicy(self)
 
     @staticmethod
@@ -128,7 +131,7 @@ def sample_action(self, x: Observation) -> NDArray:
     def predict_value(self, x: Observation, action: NDArray) -> NDArray:
         raise NotImplementedError
 
-    def inner_update(self, batch: TorchMiniBatch) -> Dict[str, float]:
+    def inner_update(self, batch: TorchMiniBatch) -> dict[str, float]:
         raise NotImplementedError
 
     def get_action_type(self) -> ActionSpace:

d3rlpy/algos/qlearning/torch/bc_impl.py

@@ -1,6 +1,6 @@
 import dataclasses
 from abc import ABCMeta, abstractmethod
-from typing import Callable, Dict, Union
+from typing import Callable, Union
 
 import torch
 from torch.optim import Optimizer
@@ -60,7 +60,7 @@ def compute_imitator_grad(self, batch: TorchMiniBatch) -> ImitationLoss:
         loss.loss.backward()
         return loss
 
-    def update_imitator(self, batch: TorchMiniBatch) -> Dict[str, float]:
+    def update_imitator(self, batch: TorchMiniBatch) -> dict[str, float]:
         loss = self._compute_imitator_grad(batch)
         self._modules.optim.step()
         return asdict_as_float(loss)
@@ -81,7 +81,7 @@ def inner_predict_value(
 
     def inner_update(
         self, batch: TorchMiniBatch, grad_step: int
-    ) -> Dict[str, float]:
+    ) -> dict[str, float]:
         return self.update_imitator(batch)
 
 

d3rlpy/algos/qlearning/torch/bcq_impl.py

@@ -1,6 +1,6 @@
 import dataclasses
 import math
-from typing import Callable, Dict, cast
+from typing import Callable, cast
 
 import torch
 import torch.nn.functional as F
@@ -50,7 +50,7 @@ class BCQModules(DDPGBaseModules):
 
 class BCQImpl(DDPGBaseImpl):
     _modules: BCQModules
-    _compute_imitator_grad: Callable[[TorchMiniBatch], Dict[str, torch.Tensor]]
+    _compute_imitator_grad: Callable[[TorchMiniBatch], dict[str, torch.Tensor]]
     _lam: float
     _n_action_samples: int
     _action_flexibility: float
@@ -124,7 +124,7 @@ def compute_actor_grad(self, batch: TorchMiniBatch) -> DDPGBaseActorLoss:
 
     def compute_imitator_grad(
         self, batch: TorchMiniBatch
-    ) -> Dict[str, torch.Tensor]:
+    ) -> dict[str, torch.Tensor]:
         self._modules.vae_optim.zero_grad()
         loss = compute_vae_error(
             vae_encoder=self._modules.vae_encoder,
@@ -136,7 +136,7 @@ def compute_imitator_grad(
         loss.backward()
         return {"loss": loss}
 
-    def update_imitator(self, batch: TorchMiniBatch) -> Dict[str, float]:
+    def update_imitator(self, batch: TorchMiniBatch) -> dict[str, float]:
         loss = self._compute_imitator_grad(batch)
         self._modules.vae_optim.step()
         return {"vae_loss": float(loss["loss"].cpu().detach().numpy())}
@@ -214,7 +214,7 @@ def update_actor_target(self) -> None:
 
     def inner_update(
         self, batch: TorchMiniBatch, grad_step: int
-    ) -> Dict[str, float]:
+    ) -> dict[str, float]:
         metrics = {}
 
         metrics.update(self.update_imitator(batch))

d3rlpy/algos/qlearning/torch/bear_impl.py

@@ -1,5 +1,5 @@
 import dataclasses
-from typing import Callable, Dict, Optional
+from typing import Callable, Optional
 
 import torch
 
@@ -61,9 +61,9 @@ class BEARActorLoss(SACActorLoss):
 class BEARImpl(SACImpl):
     _modules: BEARModules
     _compute_warmup_actor_grad: Callable[
-        [TorchMiniBatch], Dict[str, torch.Tensor]
+        [TorchMiniBatch], dict[str, torch.Tensor]
     ]
-    _compute_imitator_grad: Callable[[TorchMiniBatch], Dict[str, torch.Tensor]]
+    _compute_imitator_grad: Callable[[TorchMiniBatch], dict[str, torch.Tensor]]
     _alpha_threshold: float
     _lam: float
     _n_action_samples: int
@@ -143,13 +143,13 @@ def compute_actor_loss(
 
     def compute_warmup_actor_grad(
         self, batch: TorchMiniBatch
-    ) -> Dict[str, torch.Tensor]:
+    ) -> dict[str, torch.Tensor]:
         self._modules.actor_optim.zero_grad()
         loss = self._compute_mmd_loss(batch.observations)
         loss.backward()
         return {"loss": loss}
 
-    def warmup_actor(self, batch: TorchMiniBatch) -> Dict[str, float]:
+    def warmup_actor(self, batch: TorchMiniBatch) -> dict[str, float]:
         loss = self._compute_warmup_actor_grad(batch)
         self._modules.actor_optim.step()
         return {"actor_loss": float(loss["loss"].cpu().detach().numpy())}
@@ -161,13 +161,13 @@ def _compute_mmd_loss(self, obs_t: TorchObservation) -> torch.Tensor:
 
     def compute_imitator_grad(
         self, batch: TorchMiniBatch
-    ) -> Dict[str, torch.Tensor]:
+    ) -> dict[str, torch.Tensor]:
         self._modules.vae_optim.zero_grad()
         loss = self.compute_imitator_loss(batch)
         loss.backward()
         return {"loss": loss}
 
-    def update_imitator(self, batch: TorchMiniBatch) -> Dict[str, float]:
+    def update_imitator(self, batch: TorchMiniBatch) -> dict[str, float]:
         loss = self._compute_imitator_grad(batch)
         self._modules.vae_optim.step()
         return {"imitator_loss": float(loss["loss"].cpu().detach().numpy())}
@@ -301,7 +301,7 @@ def inner_predict_best_action(self, x: TorchObservation) -> torch.Tensor:
 
     def inner_update(
         self, batch: TorchMiniBatch, grad_step: int
-    ) -> Dict[str, float]:
+    ) -> dict[str, float]:
         metrics = {}
         metrics.update(self.update_imitator(batch))
         metrics.update(self.update_critic(batch))

d3rlpy/algos/qlearning/torch/cal_ql_impl.py

@@ -1,4 +1,3 @@
-from typing import Tuple
 
 import torch
 
@@ -14,7 +13,7 @@ def _compute_policy_is_values(
         policy_obs: TorchObservation,
         value_obs: TorchObservation,
         returns_to_go: torch.Tensor,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
+    ) -> tuple[torch.Tensor, torch.Tensor]:
         values, log_probs = super()._compute_policy_is_values(
             policy_obs=policy_obs,
             value_obs=value_obs,

d3rlpy/algos/qlearning/torch/cql_impl.py

@@ -1,6 +1,6 @@
 import dataclasses
 import math
-from typing import Optional, Tuple
+from typing import Optional
 
 import torch
 import torch.nn.functional as F
@@ -119,7 +119,7 @@ def _compute_policy_is_values(
         policy_obs: TorchObservation,
         value_obs: TorchObservation,
         returns_to_go: torch.Tensor,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
+    ) -> tuple[torch.Tensor, torch.Tensor]:
         return sample_q_values_with_policy(
             policy=self._modules.policy,
             q_func_forwarder=self._q_func_forwarder,
@@ -131,7 +131,7 @@ def _compute_policy_is_values(
 
     def _compute_random_is_values(
         self, obs: TorchObservation
-    ) -> Tuple[torch.Tensor, float]:
+    ) -> tuple[torch.Tensor, float]:
         # (batch, observation) -> (batch, n, observation)
         repeated_obs = expand_and_repeat_recursively(
             obs, self._n_action_samples

d3rlpy/algos/qlearning/torch/crr_impl.py

@@ -1,5 +1,4 @@
 import dataclasses
-from typing import Dict
 
 import torch
 import torch.nn.functional as F
@@ -186,7 +185,7 @@ def update_actor_target(self) -> None:
 
     def inner_update(
         self, batch: TorchMiniBatch, grad_step: int
-    ) -> Dict[str, float]:
+    ) -> dict[str, float]:
         metrics = {}
         metrics.update(self.update_critic(batch))
         metrics.update(self.update_actor(batch))

d3rlpy/algos/qlearning/torch/ddpg_impl.py

@@ -1,6 +1,6 @@
 import dataclasses
 from abc import ABCMeta, abstractmethod
-from typing import Callable, Dict
+from typing import Callable
 
 import torch
 from torch import nn
@@ -105,7 +105,7 @@ def compute_critic_grad(self, batch: TorchMiniBatch) -> DDPGBaseCriticLoss:
         loss.critic_loss.backward()
         return loss
 
-    def update_critic(self, batch: TorchMiniBatch) -> Dict[str, float]:
+    def update_critic(self, batch: TorchMiniBatch) -> dict[str, float]:
         loss = self._compute_critic_grad(batch)
         self._modules.critic_optim.step()
         return asdict_as_float(loss)
@@ -130,7 +130,7 @@ def compute_actor_grad(self, batch: TorchMiniBatch) -> DDPGBaseActorLoss:
         loss.actor_loss.backward()
         return loss
 
-    def update_actor(self, batch: TorchMiniBatch) -> Dict[str, float]:
+    def update_actor(self, batch: TorchMiniBatch) -> dict[str, float]:
         # Q function should be inference mode for stability
         self._modules.q_funcs.eval()
         loss = self._compute_actor_grad(batch)
@@ -139,7 +139,7 @@ def update_actor(self, batch: TorchMiniBatch) -> Dict[str, float]:
 
     def inner_update(
         self, batch: TorchMiniBatch, grad_step: int
-    ) -> Dict[str, float]:
+    ) -> dict[str, float]:
         metrics = {}
         metrics.update(self.update_critic(batch))
         metrics.update(self.update_actor(batch))
@@ -241,7 +241,7 @@ def update_actor_target(self) -> None:
 
     def inner_update(
         self, batch: TorchMiniBatch, grad_step: int
-    ) -> Dict[str, float]:
+    ) -> dict[str, float]:
         metrics = super().inner_update(batch, grad_step)
         self.update_actor_target()
         return metrics

d3rlpy/algos/qlearning/torch/dqn_impl.py

@@ -1,5 +1,5 @@
 import dataclasses
-from typing import Callable, Dict
+from typing import Callable
 
 import torch
 from torch import nn
@@ -79,7 +79,7 @@ def compute_grad(self, batch: TorchMiniBatch) -> DQNLoss:
 
     def inner_update(
         self, batch: TorchMiniBatch, grad_step: int
-    ) -> Dict[str, float]:
+    ) -> dict[str, float]:
         loss = self._compute_grad(batch)
         self._modules.optim.step()
         if grad_step % self._target_update_interval == 0: