Fix QR-DQN type hints (#170)

docs/misc/changelog.rst

@@ -3,7 +3,7 @@
 Changelog
 ==========
 
-Release 1.8.0a10 (WIP)
+Release 1.8.0a13 (WIP)
 --------------------------
 
 Breaking Changes:
@@ -24,7 +24,8 @@ Others:
 ^^^^^^^
 - Moved to pyproject.toml (except flake8)
 - Added github issue forms
-- Fix Atari Roms download in CI
+- Fixed Atari Roms download in CI
+- Fixed ``sb3_contrib/qrdqn/*.py`` type hints
 
 Documentation:
 ^^^^^^^^^^^^^^

pyproject.toml

@@ -29,8 +29,6 @@ show_error_codes = true
 exclude = """(?x)(
 	sb3_contrib/ppo_recurrent/ppo_recurrent.py$
 	| sb3_contrib/ars/ars.py$
-	| sb3_contrib/qrdqn/qrdqn.py$
-	| sb3_contrib/qrdqn/policies.py$
 	| sb3_contrib/common/recurrent/policies.py$
 	| sb3_contrib/common/recurrent/buffers.py$
 	| sb3_contrib/common/maskable/distributions.py$

sb3_contrib/qrdqn/policies.py

@@ -31,7 +31,7 @@ class QuantileNetwork(BasePolicy):
         self,
         observation_space: spaces.Space,
         action_space: spaces.Space,
-        features_extractor: nn.Module,
+        features_extractor: BaseFeaturesExtractor,
         features_dim: int,
         n_quantiles: int = 200,
         net_arch: Optional[List[int]] = None,
@@ -50,7 +50,6 @@ class QuantileNetwork(BasePolicy):
 
         self.net_arch = net_arch
         self.activation_fn = activation_fn
-        self.features_extractor = features_extractor
         self.features_dim = features_dim
         self.n_quantiles = n_quantiles
         action_dim = self.action_space.n  # number of actions
@@ -64,6 +63,8 @@ class QuantileNetwork(BasePolicy):
         :param obs: Observation
         :return: The estimated quantiles for each action.
         """
+        # For type checker:
+        assert isinstance(self.features_extractor, BaseFeaturesExtractor)
         quantiles = self.quantile_net(self.extract_features(obs, self.features_extractor))
         return quantiles.view(-1, self.n_quantiles, self.action_space.n)
 
@@ -152,7 +153,8 @@ class QRDQNPolicy(BasePolicy):
             "normalize_images": normalize_images,
         }
 
-        self.quantile_net, self.quantile_net_target = None, None
+        self.quantile_net: QuantileNetwork
+        self.quantile_net_target: QuantileNetwork
         self._build(lr_schedule)
 
     def _build(self, lr_schedule: Schedule) -> None:
@@ -168,7 +170,11 @@ class QRDQNPolicy(BasePolicy):
         self.quantile_net_target.set_training_mode(False)
 
         # Setup optimizer with initial learning rate
-        self.optimizer = self.optimizer_class(self.parameters(), lr=lr_schedule(1), **self.optimizer_kwargs)
+        self.optimizer = self.optimizer_class(  # type: ignore[call-arg]
+            self.parameters(),
+            lr=lr_schedule(1),
+            **self.optimizer_kwargs,
+        )
 
     def make_quantile_net(self) -> QuantileNetwork:
         # Make sure we always have separate networks for features extractors etc

sb3_contrib/qrdqn/qrdqn.py

@@ -6,9 +6,8 @@ from gym import spaces
 from stable_baselines3.common.buffers import ReplayBuffer
 from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
 from stable_baselines3.common.policies import BasePolicy
-from stable_baselines3.common.preprocessing import maybe_transpose
 from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
-from stable_baselines3.common.utils import get_linear_fn, get_parameters_by_name, is_vectorized_observation, polyak_update
+from stable_baselines3.common.utils import get_linear_fn, get_parameters_by_name, polyak_update
 
 from sb3_contrib.common.utils import quantile_huber_loss
 from sb3_contrib.qrdqn.policies import CnnPolicy, MlpPolicy, MultiInputPolicy, QRDQNPolicy
@@ -71,12 +70,12 @@ class QRDQN(OffPolicyAlgorithm):
         learning_rate: Union[float, Schedule] = 5e-5,
         buffer_size: int = 1000000,  # 1e6
         learning_starts: int = 50000,
-        batch_size: Optional[int] = 32,
+        batch_size: int = 32,
         tau: float = 1.0,
         gamma: float = 0.99,
         train_freq: int = 4,
         gradient_steps: int = 1,
-        replay_buffer_class: Optional[ReplayBuffer] = None,
+        replay_buffer_class: Optional[Type[ReplayBuffer]] = None,
         replay_buffer_kwargs: Optional[Dict[str, Any]] = None,
         optimize_memory_usage: bool = False,
         target_update_interval: int = 10000,
@@ -124,8 +123,9 @@ class QRDQN(OffPolicyAlgorithm):
         # "epsilon" for the epsilon-greedy exploration
         self.exploration_rate = 0.0
         # Linear schedule will be defined in `_setup_model()`
-        self.exploration_schedule = None
-        self.quantile_net, self.quantile_net_target = None, None
+        self.exploration_schedule: Schedule
+        self.quantile_net: th.nn.Module
+        self.quantile_net_target: th.nn.Module
 
         if "optimizer_class" not in self.policy_kwargs:
             self.policy_kwargs["optimizer_class"] = th.optim.Adam
@@ -146,6 +146,8 @@ class QRDQN(OffPolicyAlgorithm):
         )
 
     def _create_aliases(self) -> None:
+        # For type checker:
+        assert isinstance(self.policy, QRDQNPolicy)
         self.quantile_net = self.policy.quantile_net
         self.quantile_net_target = self.policy.quantile_net_target
         self.n_quantiles = self.policy.n_quantiles
@@ -172,7 +174,7 @@ class QRDQN(OffPolicyAlgorithm):
         losses = []
         for _ in range(gradient_steps):
             # Sample replay buffer
-            replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)
+            replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)  # type: ignore[union-attr]
 
             with th.no_grad():
                 # Compute the quantiles of next observation
@@ -214,7 +216,7 @@ class QRDQN(OffPolicyAlgorithm):
 
     def predict(
         self,
-        observation: np.ndarray,
+        observation: Union[np.ndarray, Dict[str, np.ndarray]],
         state: Optional[Tuple[np.ndarray, ...]] = None,
         episode_start: Optional[np.ndarray] = None,
         deterministic: bool = False,
@@ -233,8 +235,8 @@ class QRDQN(OffPolicyAlgorithm):
             (used in recurrent policies)
         """
         if not deterministic and np.random.rand() < self.exploration_rate:
-            if is_vectorized_observation(maybe_transpose(observation, self.observation_space), self.observation_space):
-                if isinstance(self.observation_space, spaces.Dict):
+            if self.policy.is_vectorized_observation(observation):
+                if isinstance(observation, dict):
                     n_batch = observation[list(observation.keys())[0]].shape[0]
                 else:
                     n_batch = observation.shape[0]

sb3_contrib/version.txt

@@ -1 +1 @@
-1.8.0a10
+1.8.0a13

setup.py

@@ -65,7 +65,7 @@ setup(
     packages=[package for package in find_packages() if package.startswith("sb3_contrib")],
     package_data={"sb3_contrib": ["py.typed", "version.txt"]},
     install_requires=[
-        "stable_baselines3>=1.8.0a10",
+        "stable_baselines3>=1.8.0a13",
     ],
     description="Contrib package of Stable Baselines3, experimental code.",
     author="Antonin Raffin",