diff --git a/pyproject.toml b/pyproject.toml
index 2f19c2c0..3745357d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -30,6 +30,7 @@ exclude = """(?x)(
 	| sb3_contrib/ars/ars.py$
 	| sb3_contrib/common/recurrent/policies.py$
 	| sb3_contrib/common/recurrent/buffers.py$
+	| sb3_contrib/common/torch_layers.py$
 	| tests/test_train_eval_mode.py$
   )"""
 
diff --git a/sb3_contrib/common/recurrent/buffers.py b/sb3_contrib/common/recurrent/buffers.py
index 5386db11..8cdc525a 100644
--- a/sb3_contrib/common/recurrent/buffers.py
+++ b/sb3_contrib/common/recurrent/buffers.py
@@ -228,6 +228,11 @@ def _get_samples(
         lstm_states_pi = (self.to_torch(lstm_states_pi[0]).contiguous(), self.to_torch(lstm_states_pi[1]).contiguous())
         lstm_states_vf = (self.to_torch(lstm_states_vf[0]).contiguous(), self.to_torch(lstm_states_vf[1]).contiguous())
 
+        # See issue GH#284
+        episode_starts = np.logical_or(self.episode_starts[batch_inds], env_change[batch_inds]).astype(
+            self.episode_starts.dtype
+        )
+
         return RecurrentRolloutBufferSamples(
             # (batch_size, obs_dim) -> (n_seq, max_length, obs_dim) -> (n_seq * max_length, obs_dim)
             observations=self.pad(self.observations[batch_inds]).reshape((padded_batch_size, *self.obs_shape)),
@@ -237,7 +242,7 @@ def _get_samples(
             advantages=self.pad_and_flatten(self.advantages[batch_inds]),
             returns=self.pad_and_flatten(self.returns[batch_inds]),
             lstm_states=RNNStates(lstm_states_pi, lstm_states_vf),
-            episode_starts=self.pad_and_flatten(self.episode_starts[batch_inds]),
+            episode_starts=self.pad_and_flatten(episode_starts),
             mask=self.pad_and_flatten(np.ones_like(self.returns[batch_inds])),
         )
 
@@ -372,6 +377,10 @@ def _get_samples(
         observations = {key: self.pad(obs[batch_inds]) for (key, obs) in self.observations.items()}
         observations = {key: obs.reshape((padded_batch_size,) + self.obs_shape[key]) for (key, obs) in observations.items()}
 
+        episode_starts = np.logical_or(self.episode_starts[batch_inds], env_change[batch_inds]).astype(
+            self.episode_starts.dtype
+        )
+
         return RecurrentDictRolloutBufferSamples(
             observations=observations,
             actions=self.pad(self.actions[batch_inds]).reshape((padded_batch_size,) + self.actions.shape[1:]),
@@ -380,6 +389,6 @@ def _get_samples(
             advantages=self.pad_and_flatten(self.advantages[batch_inds]),
             returns=self.pad_and_flatten(self.returns[batch_inds]),
             lstm_states=RNNStates(lstm_states_pi, lstm_states_vf),
-            episode_starts=self.pad_and_flatten(self.episode_starts[batch_inds]),
+            episode_starts=self.pad_and_flatten(episode_starts),
             mask=self.pad_and_flatten(np.ones_like(self.returns[batch_inds])),
         )