diff --git a/torchy_baselines/common/base_class.py b/torchy_baselines/common/base_class.py
index 0904932..6adc45c 100644
--- a/torchy_baselines/common/base_class.py
+++ b/torchy_baselines/common/base_class.py
@@ -129,6 +129,14 @@ class BaseRLModel(object):
         self._current_progress = 1.0 - float(num_timesteps) / float(total_timesteps)
 
     def _update_learning_rate(self, optimizers):
+        """
+        Update the optimizers learning rate using the current learning rate schedule
+        and the current progress (from 1 to 0).
+
+        :param optimizers: ([th.optim.Optimizer] or Optimizer) An optimizer
+            or a list of optimizer.
+        """
+        # Log the current learning rate
         logger.logkv("learning_rate", self.learning_rate(self._current_progress))
 
         if not isinstance(optimizers, list):
diff --git a/torchy_baselines/ppo/policies.py b/torchy_baselines/ppo/policies.py
index c967962..e973858 100644
--- a/torchy_baselines/ppo/policies.py
+++ b/torchy_baselines/ppo/policies.py
@@ -148,7 +148,6 @@ class PPOPolicy(BasePolicy):
                     self.value_net: 1
                 }[module]
                 module.apply(partial(self.init_weights, gain=gain))
-        # TODO: support linear decay of the learning rate
         self.optimizer = th.optim.Adam(self.parameters(), lr=learning_rate(1), eps=self.adam_epsilon)
 
     def forward(self, obs, deterministic=False):