diff --git a/mujoco_playground/experimental/learning/aloha.ipynb b/mujoco_playground/experimental/learning/aloha.ipynb
deleted file mode 100644
index 963d6c3a3..000000000
--- a/mujoco_playground/experimental/learning/aloha.ipynb
+++ /dev/null
@@ -1,199 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "xla_flags = os.environ.get(\"XLA_FLAGS\", \"\")\n",
-    "xla_flags += \" --xla_gpu_triton_gemm_any=True\"\n",
-    "os.environ[\"XLA_FLAGS\"] = xla_flags\n",
-    "os.environ[\"MUJOCO_GL\"] = \"egl\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import functools\n",
-    "import json\n",
-    "from datetime import datetime\n",
-    "\n",
-    "import jax\n",
-    "import matplotlib.pyplot as plt\n",
-    "import mediapy as media\n",
-    "from brax.training.agents.ppo import networks as ppo_networks\n",
-    "from brax.training.agents.ppo import train as ppo\n",
-    "from IPython.display import clear_output, display\n",
-    "import numpy as np\n",
-    "\n",
-    "from mujoco_playground import BraxEnvWrapper, manipulation\n",
-    "\n",
-    "# Enable persistent compilation cache.\n",
-    "jax.config.update(\"jax_compilation_cache_dir\", \"/tmp/jax_cache\")\n",
-    "jax.config.update(\"jax_persistent_cache_min_entry_size_bytes\", -1)\n",
-    "jax.config.update(\"jax_persistent_cache_min_compile_time_secs\", 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "env_name = \"AlohaSinglePegInsertion\"\n",
-    "env_cfg = manipulation.get_default_config(env_name)\n",
-    "env = manipulation.load(env_name, config=env_cfg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from mujoco_playground.learning import manipulation_params\n",
-    "\n",
-    "ppo_params = manipulation_params.brax_ppo_config(env_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x_data, y_data, y_dataerr = [], [], []\n",
-    "times = [datetime.now()]\n",
-    "\n",
-    "\n",
-    "def progress(num_steps, metrics):\n",
-    "  clear_output(wait=True)\n",
-    "\n",
-    "  times.append(datetime.now())\n",
-    "  x_data.append(num_steps)\n",
-    "  y_data.append(metrics[\"eval/episode_reward\"])\n",
-    "  y_dataerr.append(metrics[\"eval/episode_reward_std\"])\n",
-    "\n",
-    "  plt.xlim([0, ppo_params[\"num_timesteps\"] * 1.25])\n",
-    "  plt.ylim([0, 15_000])\n",
-    "  plt.xlabel(\"# environment steps\")\n",
-    "  plt.ylabel(\"reward per episode\")\n",
-    "  plt.title(f\"y={y_data[-1]:.3f}\")\n",
-    "  plt.errorbar(x_data, y_data, yerr=y_dataerr, color=\"blue\")\n",
-    "\n",
-    "  display(plt.gcf())\n",
-    "\n",
-    "\n",
-    "training_params = dict(ppo_params)\n",
-    "del training_params[\"network_factory\"]\n",
-    "train_fn = functools.partial(ppo.train, **training_params, progress_fn=progress)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "network_factory = functools.partial(\n",
-    "    ppo_networks.make_ppo_networks,\n",
-    "    policy_hidden_layer_sizes=ppo_params.network_factory.policy_hidden_layer_sizes,\n",
-    ")\n",
-    "make_inference_fn, params, metrics = train_fn(environment=BraxEnvWrapper(env))\n",
-    "print(f\"time to jit: {times[1] - times[0]}\")\n",
-    "print(f\"time to train: {times[-1] - times[1]}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "jit_reset = jax.jit(env.reset)\n",
-    "jit_step = jax.jit(env.step)\n",
-    "jit_inference_fn = jax.jit(make_inference_fn(params, deterministic=True))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# rng = jax.random.PRNGKey(42)\n",
-    "# rollout = []\n",
-    "# n_episodes = 1\n",
-    "\n",
-    "# for _ in range(n_episodes):\n",
-    "#   state = jit_reset(rng)\n",
-    "#   rollout.append(state)\n",
-    "#   for i in range(env_cfg.episode_length):\n",
-    "#     act_rng, rng = jax.random.split(rng)\n",
-    "#     ctrl, _ = jit_inference_fn(state.obs, act_rng)\n",
-    "#     state = jit_step(state, ctrl)\n",
-    "#     rollout.append(state)\n",
-    "\n",
-    "render_every = 1\n",
-    "frames = env.render(\n",
-    "    rollout[::render_every],\n",
-    "    camera=\"teleoperator_pov\",\n",
-    "    height=480 * 2,\n",
-    "    width=640 * 2,\n",
-    ")\n",
-    "rewards = [s.reward for s in rollout]\n",
-    "media.show_video(frames, fps=1.0 / env.dt / render_every)\n",
-    "\n",
-    "plt.plot(np.convolve(rewards, np.ones(100) / 100, mode=\"valid\"))\n",
-    "plt.xlabel(\"time step\")\n",
-    "plt.ylabel(\"reward\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.15"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}