Ensure train/n_updates metric accounts for early stopping of training loop (#1311)

* Correct _n_updates when target_kl stops loop early

* Update changelog

* Simplify code

---------

Co-authored-by: Antonin Raffin <antonin.raffin@ensta.org>

docs/misc/changelog.rst

@@ -26,6 +26,7 @@ New Features:
 Bug Fixes:
 ^^^^^^^^^^
 - Fixed Atari wrapper that missed the reset condition (@luizapozzobon)
+- Fixed PPO train/n_updates metric not accounting for early stopping (@adamfrly)
 
 Deprecations:
 ^^^^^^^^^^^^^

stable_baselines3/ppo/ppo.py

@@ -189,7 +189,6 @@ class PPO(OnPolicyAlgorithm):
         clip_fractions = []
 
         continue_training = True
-
         # train for n_epochs epochs
         for epoch in range(self.n_epochs):
             approx_kl_divs = []
@@ -271,10 +270,10 @@ class PPO(OnPolicyAlgorithm):
                 th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
                 self.policy.optimizer.step()
 
+            self._n_updates += 1
             if not continue_training:
                 break
 
-        self._n_updates += self.n_epochs
         explained_var = explained_variance(self.rollout_buffer.values.flatten(), self.rollout_buffer.returns.flatten())
 
         # Logs