Update QR-DQN optimizer to only use q_net parameters (#252)

* Updated QR-DQN optimizer input to only include quantile_net parameters

* Fix QR-DQN paper link in docs and update changelog

docs/misc/changelog.rst

@@ -16,6 +16,8 @@ New Features:
 
 Bug Fixes:
 ^^^^^^^^^^
+- Updated QR-DQN optimizer input to only include quantile_net parameters (@corentinlger)
+- Updated QR-DQN paper link in docs (@corentinlger)
 
 Deprecations:
 ^^^^^^^^^^^^^
@@ -580,4 +582,4 @@ Contributors:
 -------------
 
 @ku2482 @guyk1971 @minhlong94 @ayeright @kronion @glmcdona @cyprienc @sgillen @Gregwar @rnederstigt @qgallouedec
-@mlodel @CppMaster @burakdmb @honglu2875 @ZikangXiong @AlexPasqua @jonasreiher @icheered @Armandpl
+@mlodel @CppMaster @burakdmb @honglu2875 @ZikangXiong @AlexPasqua @jonasreiher @icheered @Armandpl @corentinlger

docs/modules/qrdqn.rst

@@ -24,7 +24,7 @@ instead of predicting the mean return (DQN).
 Notes
 -----
 
-- Original paper: https://arxiv.org/abs/1710.100442
+- Original paper: https://arxiv.org/abs/1710.10044
 - Distributional RL (C51): https://arxiv.org/abs/1707.06887
 - Further reference: https://github.com/amy12xx/ml_notes_and_reports/blob/master/distributional_rl/QRDQN.pdf
 

sb3_contrib/qrdqn/policies.py

@@ -171,7 +171,7 @@ def _build(self, lr_schedule: Schedule) -> None:
 
         # Setup optimizer with initial learning rate
         self.optimizer = self.optimizer_class(  # type: ignore[call-arg]
-            self.parameters(),
+            self.quantile_net.parameters(),
             lr=lr_schedule(1),
             **self.optimizer_kwargs,
         )