diff --git a/src/evox/problems/neuroevolution/reinforcement_learning/brax.py b/src/evox/problems/neuroevolution/reinforcement_learning/brax.py
index 71b84693..ad7e6b08 100644
--- a/src/evox/problems/neuroevolution/reinforcement_learning/brax.py
+++ b/src/evox/problems/neuroevolution/reinforcement_learning/brax.py
@@ -1,4 +1,4 @@
-from typing import Callable, Any
+from typing import Callable, Any, Optional
 from brax import envs
 from brax.io import html, image
 import jax
@@ -129,10 +129,27 @@ def visualize(
         key,
         weights,
         output_type: str = "HTML",
-        respect_done=False,
+        respect_done: bool = False,
+        num_episodes: Optional[int] = None,
         *args,
         **kwargs,
     ):
+        """Visualize the brax environment with the given policy and weights.
+
+        Parameters
+        ----------
+        key
+            The random key.
+        weights
+            The weights of the policy.
+        output_type
+            The output type, either "HTML" or "rgb_array".
+        respect_done
+            Whether to respect the done signal.
+        num_episodes
+            The number of episodes to visualize, used to override the num_episodes in the constructor.
+            If None, use the num_episodes in the constructor.
+        """
         assert output_type in [
             "HTML",
             "rgb_array",
@@ -143,8 +160,23 @@ def visualize(
         jit_env_step = jit(env.step)
         trajectory = [brax_state.pipeline_state]
         episode_length = 1
-        for _ in range(self.cap_episode):
-            action = self.policy(weights, brax_state.obs)
+
+        if self.stateful_policy:
+            rollout_state = (self.initial_state, brax_state)
+        else:
+            rollout_state = (brax_state,)
+
+        for _ in range(self.num_episodes):
+            if self.stateful_policy:
+                state, brax_state = rollout_state
+                action, state = self.policy(state, weights, brax_state.obs)
+                rollout_state = (state, brax_state)
+            else:
+                (brax_state,) = rollout_state
+                action = self.policy(weights, brax_state.obs)
+                rollout_state = (brax_state,)
+
+            trajectory.append(brax_state.pipeline_state)
             brax_state = jit_env_step(brax_state, action)
             trajectory.append(brax_state.pipeline_state)
             episode_length += 1 - brax_state.done