diff --git a/learning/README.md b/learning/README.md
index 0a24ad8..4244539 100644
--- a/learning/README.md
+++ b/learning/README.md
@@ -10,6 +10,21 @@ For more detailed tutorials on using MuJoCo Playground for RL, see:
 4. Training CartPole from Vision [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-deepmind/mujoco_playground/blob/main/learning/notebooks/training_vision_1.ipynb)
 5. Robotic Manipulation from Vision [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-deepmind/mujoco_playground/blob/main/learning/notebooks/training_vision_2.ipynb)
 
+## Training with brax PPO
+
+To train with brax PPO, you can use the `train_jax_ppo.py` script. This script uses the brax PPO algorithm to train an agent on a given environment.
+
+```bash
+python train_jax_ppo.py --env_name=CartpoleBalance
+```
+
+To train a vision-based policy using pixel observations:
+```bash
+python train_jax_ppo.py --env_name=CartpoleBalance --vision
+```
+
+Use `python train_jax_ppo.py --help` to see possible options and usage. Logs and checkpoints are saved in `logs` directory.
+
 ## Training with RSL-RL
 
 To train with RSL-RL, you can use the `train_rsl_rl.py` script. This script uses the RSL-RL algorithm to train an agent on a given environment.
@@ -18,7 +33,7 @@ To train with RSL-RL, you can use the `train_rsl_rl.py` script. This script uses
 python train_rsl_rl.py --env_name=LeapCubeReorient
 ```
 
-to render the behaviour from the resulting policy:
+To render the behaviour from the resulting policy:
 ```bash
 python learning/train_rsl_rl.py --env_name LeapCubeReorient --play_only --load_run_name <run_name>
 ```
diff --git a/learning/train_jax_ppo.py b/learning/train_jax_ppo.py
index a251427..ef4a44a 100644
--- a/learning/train_jax_ppo.py
+++ b/learning/train_jax_ppo.py
@@ -198,12 +198,12 @@ def main(argv):
   if _CLIPPING_EPSILON.present:
     ppo_params.clipping_epsilon = _CLIPPING_EPSILON.value
   if _POLICY_HIDDEN_LAYER_SIZES.present:
-    ppo_params.network_factory.policy_hidden_layer_sizes = tuple(
-        _POLICY_HIDDEN_LAYER_SIZES.value
+    ppo_params.network_factory.policy_hidden_layer_sizes = list(
+        map(int, _POLICY_HIDDEN_LAYER_SIZES.value)
     )
   if _VALUE_HIDDEN_LAYER_SIZES.present:
-    ppo_params.network_factory.value_hidden_layer_sizes = tuple(
-        _VALUE_HIDDEN_LAYER_SIZES.value
+    ppo_params.network_factory.value_hidden_layer_sizes = list(
+        map(int, _VALUE_HIDDEN_LAYER_SIZES.value)
     )
   if _POLICY_OBS_KEY.present:
     ppo_params.network_factory.policy_obs_key = _POLICY_OBS_KEY.value