diff --git a/docs/use_cases/agent_simulations/gymnasium.ipynb b/docs/use_cases/agent_simulations/gymnasium.ipynb
index ad35b500..766ed21b 100644
--- a/docs/use_cases/agent_simulations/gymnasium.ipynb
+++ b/docs/use_cases/agent_simulations/gymnasium.ipynb
@@ -12,22 +12,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "f36427cf",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gymnasium in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (0.28.1)\n",
+      "Requirement already satisfied: farama-notifications>=0.0.1 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium) (0.0.4)\n",
+      "Requirement already satisfied: importlib-metadata>=4.8.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium) (6.0.1)\n",
+      "Requirement already satisfied: cloudpickle>=1.2.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium) (2.2.1)\n",
+      "Requirement already satisfied: numpy>=1.21.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium) (1.24.3)\n",
+      "Requirement already satisfied: jax-jumpy>=1.0.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium) (1.0.0)\n",
+      "Requirement already satisfied: typing-extensions>=4.3.0 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from gymnasium) (4.5.0)\n",
+      "Requirement already satisfied: zipp>=0.5 in /Users/michaelchang/.miniconda3/envs/langchain/lib/python3.9/site-packages (from importlib-metadata>=4.8.0->gymnasium) (3.15.0)\n"
+     ]
+    }
+   ],
    "source": [
     "!pip install gymnasium"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 2,
    "id": "f9bd38b4",
    "metadata": {},
    "outputs": [],
    "source": [
     "import gymnasium as gym\n",
+    "import inspect\n",
     "import tenacity\n",
     "\n",
     "from langchain.chat_models import ChatOpenAI\n",
@@ -50,20 +66,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 3,
    "id": "870c24bc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def get_docs(env):\n",
-    "    while 'env' in dir(env):\n",
-    "        env = env.env\n",
-    "    return env.__doc__\n",
-    "\n",
-    "class Agent():\n",
+    "class GymnasiumAgent():\n",
+    "    @classmethod\n",
+    "    def get_docs(cls, env):\n",
+    "        return env.unwrapped.__doc__\n",
+    "    \n",
     "    def __init__(self, model, env):\n",
     "        self.model = model\n",
-    "        self.docs = get_docs(env)\n",
+    "        self.env = env\n",
+    "        self.docs = self.get_docs(env)\n",
     "        \n",
     "        self.instructions = \"\"\"\n",
     "Your goal is to maximize your return, i.e. the sum of the rewards you receive.\n",
@@ -90,22 +106,17 @@
     "        self.message_history = []\n",
     "        self.ret = 0\n",
     "        \n",
-    "    def reset(self, obs):\n",
+    "    def random_action(self):\n",
+    "        action = self.env.action_space.sample()\n",
+    "        return action\n",
+    "        \n",
+    "    def reset(self):\n",
     "        self.message_history = [\n",
     "            SystemMessage(content=self.docs),\n",
     "            SystemMessage(content=self.instructions),\n",
     "        ]\n",
-    "        obs_message = f\"\"\"\n",
-    "Observation: {obs}\n",
-    "Reward: 0\n",
-    "Termination: False\n",
-    "Truncation: False\n",
-    "Return: 0\n",
-    "        \"\"\"\n",
-    "        self.message_history.append(HumanMessage(content=obs_message))\n",
-    "        return obs_message\n",
     "        \n",
-    "    def observe(self, obs, rew, term, trunc, info):\n",
+    "    def observe(self, obs, rew=0, term=False, trunc=False, info=None):\n",
     "        self.ret += rew\n",
     "    \n",
     "        obs_message = f\"\"\"\n",
@@ -117,16 +128,25 @@
     "        \"\"\"\n",
     "        self.message_history.append(HumanMessage(content=obs_message))\n",
     "        return obs_message\n",
-    "        \n",
-    "    @tenacity.retry(stop=tenacity.stop_after_attempt(2),\n",
-    "                    wait=tenacity.wait_none(),  # No waiting time between retries\n",
-    "                    retry=tenacity.retry_if_exception_type(ValueError),\n",
-    "                    before_sleep=lambda retry_state: print(f\"ValueError occurred: {retry_state.outcome.exception()}, retrying...\"),\n",
-    "                    retry_error_callback=lambda retry_state: 0) # Default value when all retries are exhausted\n",
-    "    def act(self):\n",
+    "    \n",
+    "    def _act(self):\n",
     "        act_message = self.model(self.message_history)\n",
     "        self.message_history.append(act_message)\n",
     "        action = int(self.action_parser.parse(act_message.content)['action'])\n",
+    "        return action\n",
+    "        \n",
+    "    def act(self):\n",
+    "        try:\n",
+    "            for attempt in tenacity.Retrying(\n",
+    "                                stop=tenacity.stop_after_attempt(2),\n",
+    "                                wait=tenacity.wait_none(),  # No waiting time between retries\n",
+    "                                retry=tenacity.retry_if_exception_type(ValueError),\n",
+    "                                before_sleep=lambda retry_state: print(f\"ValueError occurred: {retry_state.outcome.exception()}, retrying...\"),\n",
+    "                            ):\n",
+    "                with attempt:\n",
+    "                    action = self._act()\n",
+    "        except tenacity.RetryError as e:\n",
+    "            action = self.random_action()\n",
     "        return action"
    ]
   },
@@ -140,13 +160,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 4,
    "id": "9e902cfd",
    "metadata": {},
    "outputs": [],
    "source": [
     "env = gym.make(\"Blackjack-v1\")\n",
-    "agent = Agent(model=ChatOpenAI(temperature=0.2), env=env)"
+    "agent = GymnasiumAgent(model=ChatOpenAI(temperature=0.2), env=env)"
    ]
   },
   {
@@ -159,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 5,
    "id": "ad361210",
    "metadata": {},
    "outputs": [
@@ -168,7 +188,7 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Observation: (10, 3, 0)\n",
+      "Observation: (15, 4, 0)\n",
       "Reward: 0\n",
       "Termination: False\n",
       "Truncation: False\n",
@@ -176,19 +196,11 @@
       "        \n",
       "Action: 1\n",
       "\n",
-      "Observation: (18, 3, 0)\n",
-      "Reward: 0.0\n",
-      "Termination: False\n",
-      "Truncation: False\n",
-      "Return: 0.0\n",
-      "        \n",
-      "Action: 0\n",
-      "\n",
-      "Observation: (18, 3, 0)\n",
-      "Reward: 1.0\n",
+      "Observation: (25, 4, 0)\n",
+      "Reward: -1.0\n",
       "Termination: True\n",
       "Truncation: False\n",
-      "Return: 1.0\n",
+      "Return: -1.0\n",
       "        \n",
       "break True False\n"
      ]
@@ -196,14 +208,18 @@
    ],
    "source": [
     "observation, info = env.reset()\n",
-    "obs_message = agent.reset(observation)\n",
+    "agent.reset()\n",
+    "\n",
+    "obs_message = agent.observe(observation)\n",
     "print(obs_message)\n",
+    "\n",
     "while True:\n",
     "    action = agent.act()\n",
     "    observation, reward, termination, truncation, info = env.step(action)\n",
     "    obs_message = agent.observe(observation, reward, termination, truncation, info)\n",
     "    print(f'Action: {action}')\n",
     "    print(obs_message)\n",
+    "    \n",
     "    if termination or truncation:\n",
     "        print('break', termination, truncation)\n",
     "        break\n",