diff --git a/lec6.ipynb b/lec6.ipynb
new file mode 100644
index 0000000..0b09299
--- /dev/null
+++ b/lec6.ipynb
@@ -0,0 +1,549 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[['_' '_' 'X']\n",
+      " ['X' '' '']]\n",
+      "[[0. 0. 0.]\n",
+      " [0. 0. 0.]\n",
+      " [0. 0. 0.]]\n",
+      "[['_' '_' 'X']\n",
+      " ['X' '' '']]\n",
+      "[[-0.1  0.   0. ]\n",
+      " [ 0.   0.   0. ]\n",
+      " [ 0.   0.   0. ]]\n",
+      "[['_' '_' 'X']\n",
+      " ['' 'X' '']]\n",
+      "[[-0.1 -0.1  0. ]\n",
+      " [ 0.   0.   0. ]\n",
+      " [ 0.   0.   0. ]]\n",
+      "[['_' '_' 'X']\n",
+      " ['X' '' '']]\n",
+      "[[-0.1 -0.1  0. ]\n",
+      " [-0.1  0.   0. ]\n",
+      " [ 0.   0.   0. ]]\n",
+      "[['_' '_' 'X']\n",
+      " ['' '' 'X']]\n",
+      "[[-0.1 -0.1  0. ]\n",
+      " [-0.1  0.   0. ]\n",
+      " [ 0.   0.   0. ]]\n",
+      "Episode 1 is done\n",
+      "[['_' '_' 'X']\n",
+      " ['X' '' '']]\n",
+      "[[-0.19 -0.1   0.  ]\n",
+      " [-0.1   0.    0.  ]\n",
+      " [ 0.    0.    0.  ]]\n",
+      "[['_' '_' 'X']\n",
+      " ['' '' 'X']]\n",
+      "[[-0.19 -0.1   0.  ]\n",
+      " [-0.1   0.    0.  ]\n",
+      " [ 0.    0.    0.  ]]\n",
+      "Episode 2 is done\n",
+      "[['_' '_' 'X']\n",
+      " ['' '' 'X']]\n",
+      "[[-0.19 -0.1   0.  ]\n",
+      " [-0.1   0.    0.  ]\n",
+      " [ 0.    0.    0.  ]]\n",
+      "Episode 3 is done\n",
+      "[['_' '_' 'X']\n",
+      " ['' '' 'X']]\n",
+      "[[-0.19 -0.1   0.  ]\n",
+      " [-0.1   0.    0.  ]\n",
+      " [ 0.    0.    0.  ]]\n",
+      "Episode 4 is done\n",
+      "[['_' '_' 'X']\n",
+      " ['' '' 'X']]\n",
+      "[[-0.19 -0.1   0.  ]\n",
+      " [-0.1   0.    0.  ]\n",
+      " [ 0.    0.    0.  ]]\n",
+      "Episode 5 is done\n",
+      "[['_' '_' 'X']\n",
+      " ['X' '' '']]\n",
+      "[[-0.271 -0.1    0.   ]\n",
+      " [-0.1    0.     0.   ]\n",
+      " [ 0.     0.     0.   ]]\n",
+      "[['_' '_' 'X']\n",
+      " ['' '' 'X']]\n",
+      "[[-0.271 -0.1    0.   ]\n",
+      " [-0.1    0.     0.   ]\n",
+      " [ 0.     0.     0.   ]]\n",
+      "Episode 6 is done\n",
+      "[['_' '_' 'X']\n",
+      " ['' '' 'X']]\n",
+      "[[-0.271 -0.1    0.   ]\n",
+      " [-0.1    0.     0.   ]\n",
+      " [ 0.     0.     0.   ]]\n",
+      "Episode 7 is done\n",
+      "[['_' '_' 'X']\n",
+      " ['' '' 'X']]\n",
+      "[[-0.271 -0.1    0.   ]\n",
+      " [-0.1    0.     0.   ]\n",
+      " [ 0.     0.     0.   ]]\n",
+      "Episode 8 is done\n",
+      "[['_' '_' 'X']\n",
+      " ['' '' 'X']]\n",
+      "[[-0.271 -0.1    0.   ]\n",
+      " [-0.1    0.     0.   ]\n",
+      " [ 0.     0.     0.   ]]\n",
+      "Episode 9 is done\n",
+      "[['_' '_' 'X']\n",
+      " ['' '' 'X']]\n",
+      "[[-0.271 -0.1    0.   ]\n",
+      " [-0.1    0.     0.   ]\n",
+      " [ 0.     0.     0.   ]]\n",
+      "Episode 10 is done\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def set_state(Q, map, state):\n",
+    "    map[1] = [\"\", \"\", \"\"]\n",
+    "    map[1][state] = \"X\"\n",
+    "    print(map)\n",
+    "    print(Q)\n",
+    "\n",
+    "Q = np.zeros([3, 3])\n",
+    "map = np.array([[\"_\", \"_\", \"X\"], [\"\", \"\", \"\"]])\n",
+    "\n",
+    "max_steps = 10\n",
+    "alpha = 0.1  # коэф. обучения\n",
+    "gamma = 0.9  # коэф. дисконтирования\n",
+    "epsilon = 0.1  # параметр исследования vs. эксплуатации\n",
+    "\n",
+    "actions = {\"left\": -1, \"right\": 1}\n",
+    "\n",
+    "for episode in range(max_steps):  # колво эпизодов обучения\n",
+    "    state = 0  # стартовое состояние\n",
+    "    if episode == 0:\n",
+    "        set_state(Q, map, state)\n",
+    "\n",
+    "    while state != 2:  # пока не достигнута цель\n",
+    "        # выбор действия\n",
+    "        if np.random.rand() < epsilon:\n",
+    "            direction = actions[list(actions.keys())[np.random.randint(0, 1)]]\n",
+    "            action = np.clip(state + direction, 0, 2)  # случайное действие\n",
+    "        else:\n",
+    "            action = np.argmax(Q[state])\n",
+    "\n",
+    "        # переход в новое состояние и получение награды\n",
+    "        new_state = action\n",
+    "        reward = (\n",
+    "            -1 if new_state != 2 else 0\n",
+    "        )  # награда -1 за каждый шаг, 0 за достижение цели\n",
+    "\n",
+    "        # обновление Q-значения\n",
+    "        Q[state, action] = (1 - alpha) * Q[state, action] + alpha * (\n",
+    "            reward + gamma * np.max(Q[new_state])\n",
+    "        )\n",
+    "\n",
+    "        state = new_state\n",
+    "        set_state(Q, map, state)\n",
+    "        \n",
+    "    print(f\"Episode {episode + 1} is done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\user\\Projects\\python\\ckmai\\.venv\\Lib\\site-packages\\gymnasium\\envs\\registration.py:642: UserWarning: \u001b[33mWARN: Overriding environment gymnasium_env/MyEnv-v1 already in registry.\u001b[0m\n",
+      "  logger.warn(f\"Overriding environment {new_spec.id} already in registry.\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "import gymnasium as gym\n",
+    "\n",
+    "\n",
+    "class MyAgent:\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        env: gym.Env,\n",
+    "        learning_rate: float,\n",
+    "        epsilon: float,\n",
+    "        discount_factor,\n",
+    "    ):\n",
+    "        self.env = env\n",
+    "        self.q_values = np.zeros([3, 3])  # type: ignore\n",
+    "\n",
+    "        self.learning_rate = learning_rate\n",
+    "        self.discount_factor = discount_factor\n",
+    "\n",
+    "        self.epsilon = epsilon\n",
+    "\n",
+    "        self.training_error = []\n",
+    "\n",
+    "    def get_action(self, state: int) -> int:\n",
+    "        if np.random.rand() < self.epsilon:\n",
+    "            return self.env.action_space.sample()\n",
+    "        else:\n",
+    "            return int(np.argmax(self.q_values[state]))\n",
+    "\n",
+    "    def update(\n",
+    "        self,\n",
+    "        state: int,\n",
+    "        action: int,\n",
+    "        reward: float,\n",
+    "        terminated: bool,\n",
+    "        new_state,\n",
+    "    ):\n",
+    "        future_q_value = (not terminated) * np.max(self.q_values[new_state])\n",
+    "        current_q_value = self.q_values[state][action]\n",
+    "        temporal_difference = (\n",
+    "            reward + self.discount_factor * future_q_value - current_q_value\n",
+    "        )\n",
+    "\n",
+    "        self.q_values[state][action] = (\n",
+    "            1 - self.learning_rate\n",
+    "        ) * current_q_value + self.learning_rate * (\n",
+    "            reward + self.discount_factor * future_q_value\n",
+    "        )\n",
+    "\n",
+    "        self.training_error.append(temporal_difference)\n",
+    "\n",
+    "    def render(self):\n",
+    "        return \"\\n\".join(\n",
+    "            [\", \".join([str(item) for item in row]) for row in self.q_values]\n",
+    "        )\n",
+    "\n",
+    "\n",
+    "class MyEnv(gym.Env):\n",
+    "    metadata = {\"render_modes\": [\"ansi\", \"rgb_array\"], \"render_fps\": 4}\n",
+    "\n",
+    "    def __init__(self, render_mode=None):\n",
+    "        self.size = 3\n",
+    "\n",
+    "        self.render_mode = render_mode\n",
+    "\n",
+    "        self._agent_location = 0\n",
+    "        self._target_location = 2\n",
+    "\n",
+    "        self.observation_space = gym.spaces.Dict(\n",
+    "            {\n",
+    "                \"agent\": gym.spaces.Discrete(1),\n",
+    "                \"target\": gym.spaces.Discrete(1),\n",
+    "            }\n",
+    "        )\n",
+    "\n",
+    "        self.action_space = gym.spaces.Discrete(2)\n",
+    "        self._action_to_direction = {\n",
+    "            0: -1,  # left\n",
+    "            1: 1,  # right\n",
+    "        }\n",
+    "\n",
+    "    def _get_obs(self):\n",
+    "        return {\n",
+    "            \"agent\": self._agent_location,\n",
+    "            \"target\": self._target_location,\n",
+    "        }\n",
+    "\n",
+    "    def _get_info(self):\n",
+    "        return {\"distance\": abs(self._agent_location - self._target_location)}\n",
+    "\n",
+    "    def reset(self, seed=None, options=None):\n",
+    "        super().reset(seed=seed)\n",
+    "        self._agent_location = 0\n",
+    "        self._target_location = 2\n",
+    "        observation = self._get_obs()\n",
+    "        info = self._get_info()\n",
+    "\n",
+    "        return observation, info\n",
+    "\n",
+    "    def step(self, action):\n",
+    "        direction = 0 if action == 2 else self._action_to_direction[action]\n",
+    "        self._agent_location = np.clip(\n",
+    "            self._agent_location + direction, 0, self.size - 1\n",
+    "        )\n",
+    "        if (action == 2):\n",
+    "            self._agent_location = self._target_location\n",
+    "\n",
+    "        terminated = self._agent_location == self._target_location\n",
+    "        truncated = False\n",
+    "        reward = -1 if not terminated else 0\n",
+    "        observation = self._get_obs()\n",
+    "        info = self._get_info()\n",
+    "\n",
+    "        return observation, reward, terminated, truncated, info\n",
+    "\n",
+    "    def render(self):\n",
+    "        map = ['_', '_', '_']\n",
+    "        map[self._agent_location] = \"A\"\n",
+    "        map[self._target_location] = \"X\"\n",
+    "        return \"   \".join(map)\n",
+    "\n",
+    "\n",
+    "env_id = \"gymnasium_env/MyEnv-v1\"\n",
+    "gym.register(\n",
+    "    id=env_id,\n",
+    "    entry_point=MyEnv,  # type: ignore\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A   _   X\n",
+      "0.0, 0.0, 0.0\n",
+      "0.0, 0.0, 0.0\n",
+      "0.0, 0.0, 0.0\n",
+      "\n",
+      "===> Start episode 1\n",
+      "\n",
+      "Start action 1\n",
+      "A   _   X\n",
+      "-0.1, 0.0, 0.0\n",
+      "0.0, 0.0, 0.0\n",
+      "0.0, 0.0, 0.0\n",
+      "\n",
+      "Start action 2\n",
+      "_   A   X\n",
+      "-0.1, -0.1, 0.0\n",
+      "0.0, 0.0, 0.0\n",
+      "0.0, 0.0, 0.0\n",
+      "\n",
+      "Start action 3\n",
+      "A   _   X\n",
+      "-0.1, -0.1, 0.0\n",
+      "-0.1, 0.0, 0.0\n",
+      "0.0, 0.0, 0.0\n",
+      "\n",
+      "Start action 4\n",
+      "_   _   X\n",
+      "-0.1, -0.1, 0.0\n",
+      "-0.1, 0.0, 0.0\n",
+      "0.0, 0.0, 0.0\n",
+      "\n",
+      "===> Episode 1 is done\n",
+      "\n",
+      "===> Start episode 2\n",
+      "\n",
+      "Start action 5\n",
+      "_   _   X\n",
+      "-0.1, -0.1, 0.0\n",
+      "-0.1, 0.0, 0.0\n",
+      "0.0, 0.0, 0.0\n",
+      "\n",
+      "===> Episode 2 is done\n",
+      "\n",
+      "===> Start episode 3\n",
+      "\n",
+      "Start action 6\n",
+      "_   _   X\n",
+      "-0.1, -0.1, 0.0\n",
+      "-0.1, 0.0, 0.0\n",
+      "0.0, 0.0, 0.0\n",
+      "\n",
+      "===> Episode 3 is done\n",
+      "\n",
+      "===> Start episode 4\n",
+      "\n",
+      "Start action 7\n",
+      "_   A   X\n",
+      "-0.1, -0.19, 0.0\n",
+      "-0.1, 0.0, 0.0\n",
+      "0.0, 0.0, 0.0\n",
+      "\n",
+      "Start action 8\n",
+      "_   _   X\n",
+      "-0.1, -0.19, 0.0\n",
+      "-0.1, 0.0, 0.0\n",
+      "0.0, 0.0, 0.0\n",
+      "\n",
+      "===> Episode 4 is done\n",
+      "\n",
+      "===> Start episode 5\n",
+      "\n",
+      "Start action 9\n",
+      "_   _   X\n",
+      "-0.1, -0.19, 0.0\n",
+      "-0.1, 0.0, 0.0\n",
+      "0.0, 0.0, 0.0\n",
+      "\n",
+      "===> Episode 5 is done\n",
+      "\n",
+      "===> Start episode 6\n",
+      "\n",
+      "Start action 10\n",
+      "_   _   X\n",
+      "-0.1, -0.19, 0.0\n",
+      "-0.1, 0.0, 0.0\n",
+      "0.0, 0.0, 0.0\n",
+      "\n",
+      "===> Episode 6 is done\n",
+      "\n",
+      "===> Start episode 7\n",
+      "\n",
+      "Start action 11\n",
+      "_   _   X\n",
+      "-0.1, -0.19, 0.0\n",
+      "-0.1, 0.0, 0.0\n",
+      "0.0, 0.0, 0.0\n",
+      "\n",
+      "===> Episode 7 is done\n",
+      "\n",
+      "===> Start episode 8\n",
+      "\n",
+      "Start action 12\n",
+      "_   _   X\n",
+      "-0.1, -0.19, 0.0\n",
+      "-0.1, 0.0, 0.0\n",
+      "0.0, 0.0, 0.0\n",
+      "\n",
+      "===> Episode 8 is done\n",
+      "\n",
+      "===> Start episode 9\n",
+      "\n",
+      "Start action 13\n",
+      "_   A   X\n",
+      "-0.1, -0.271, 0.0\n",
+      "-0.1, 0.0, 0.0\n",
+      "0.0, 0.0, 0.0\n",
+      "\n",
+      "Start action 14\n",
+      "_   _   X\n",
+      "-0.1, -0.271, 0.0\n",
+      "-0.1, 0.0, 0.0\n",
+      "0.0, 0.0, 0.0\n",
+      "\n",
+      "===> Episode 9 is done\n",
+      "\n",
+      "===> Start episode 10\n",
+      "\n",
+      "Start action 15\n",
+      "_   _   X\n",
+      "-0.1, -0.271, 0.0\n",
+      "-0.1, 0.0, 0.0\n",
+      "0.0, 0.0, 0.0\n",
+      "\n",
+      "===> Episode 10 is done\n"
+     ]
+    }
+   ],
+   "source": [
+    "from gymnasium.wrappers import RecordEpisodeStatistics\n",
+    "\n",
+    "myenv = gym.make(env_id, render_mode=\"ansi\", max_episode_steps=max_steps)\n",
+    "myenv = RecordEpisodeStatistics(myenv, buffer_length=max_steps)\n",
+    "\n",
+    "agent = MyAgent(\n",
+    "    env=myenv,\n",
+    "    learning_rate=alpha,\n",
+    "    epsilon=epsilon,\n",
+    "    discount_factor=gamma,\n",
+    ")\n",
+    "\n",
+    "action_num = 0\n",
+    "for episode in range(max_steps):\n",
+    "    obs, info = myenv.reset()\n",
+    "    done = False\n",
+    "\n",
+    "    if episode == 0:\n",
+    "        print(myenv.render())\n",
+    "        print(agent.render())\n",
+    "\n",
+    "    print(f\"\\n===> Start episode {episode + 1}\")\n",
+    "\n",
+    "    while not done:\n",
+    "        print(f\"\\nStart action {action_num + 1}\")\n",
+    "        action_num = action_num + 1\n",
+    "\n",
+    "        action = agent.get_action(obs[\"agent\"])\n",
+    "        next_obs, reward, terminated, truncated, info = myenv.step(action)\n",
+    "\n",
+    "        # update the agent\n",
+    "        agent.update(obs[\"agent\"], action, float(reward), terminated, next_obs[\"agent\"])\n",
+    "\n",
+    "        # update if the environment is done and the current obs\n",
+    "        done = terminated or truncated\n",
+    "        obs = next_obs\n",
+    "        print(myenv.render())\n",
+    "        print(agent.render())\n",
+    "\n",
+    "    print(f\"\\n===> Episode {episode + 1} is done\")\n",
+    "\n",
+    "myenv.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x800 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 3, figsize=(20, 8))\n",
+    "\n",
+    "axs[0].plot(np.convolve(myenv.return_queue, np.ones(1)))  # type: ignore\n",
+    "axs[0].set_title(\"Episode Rewards\")\n",
+    "axs[0].set_xlabel(\"Episode\")\n",
+    "axs[0].set_ylabel(\"Reward\")\n",
+    "\n",
+    "axs[1].plot(np.convolve(myenv.length_queue, np.ones(1)))  # type: ignore\n",
+    "axs[1].set_title(\"Episode Lengths\")\n",
+    "axs[1].set_xlabel(\"Episode\")\n",
+    "axs[1].set_ylabel(\"Length\")\n",
+    "\n",
+    "axs[2].plot(np.convolve(agent.training_error, np.ones(1) * -1))\n",
+    "axs[2].set_title(\"Training Error\")\n",
+    "axs[2].set_xlabel(\"Action\")\n",
+    "axs[2].set_ylabel(\"Temporal Difference\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/poetry.lock b/poetry.lock
index 7ae692c..5af1466 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -606,6 +606,17 @@ files = [
 [package.extras]
 tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"]
 
+[[package]]
+name = "farama-notifications"
+version = "0.0.4"
+description = "Notifications for all Farama Foundation maintained libraries."
+optional = false
+python-versions = "*"
+files = [
+    {file = "Farama-Notifications-0.0.4.tar.gz", hash = "sha256:13fceff2d14314cf80703c8266462ebf3733c7d165336eee998fc58e545efd18"},
+    {file = "Farama_Notifications-0.0.4-py3-none-any.whl", hash = "sha256:14de931035a41961f7c056361dc7f980762a143d05791ef5794a751a2caf05ae"},
+]
+
 [[package]]
 name = "fastjsonschema"
 version = "2.21.1"
@@ -739,6 +750,36 @@ files = [
     {file = "fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f"},
 ]
 
+[[package]]
+name = "gymnasium"
+version = "1.0.0"
+description = "A standard API for reinforcement learning and a diverse set of reference environments (formerly Gym)."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "gymnasium-1.0.0-py3-none-any.whl", hash = "sha256:b6f40e1e24c5bd419361e1a5b86a9117d2499baecc3a660d44dfff4c465393ad"},
+    {file = "gymnasium-1.0.0.tar.gz", hash = "sha256:9d2b66f30c1b34fe3c2ce7fae65ecf365d0e9982d2b3d860235e773328a3b403"},
+]
+
+[package.dependencies]
+cloudpickle = ">=1.2.0"
+farama-notifications = ">=0.0.1"
+numpy = ">=1.21.0"
+typing-extensions = ">=4.3.0"
+
+[package.extras]
+all = ["ale-py (>=0.9)", "box2d-py (==2.3.5)", "cython (<3)", "flax (>=0.5.0)", "imageio (>=2.14.1)", "jax (>=0.4.0)", "jaxlib (>=0.4.0)", "matplotlib (>=3.0)", "moviepy (>=1.0.0)", "mujoco (>=2.1.5)", "mujoco-py (>=2.1,<2.2)", "opencv-python (>=3.0)", "pygame (>=2.1.3)", "swig (==4.*)", "torch (>=1.0.0)"]
+atari = ["ale-py (>=0.9)"]
+box2d = ["box2d-py (==2.3.5)", "pygame (>=2.1.3)", "swig (==4.*)"]
+classic-control = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+jax = ["flax (>=0.5.0)", "jax (>=0.4.0)", "jaxlib (>=0.4.0)"]
+mujoco = ["imageio (>=2.14.1)", "mujoco (>=2.1.5)"]
+mujoco-py = ["cython (<3)", "cython (<3)", "mujoco-py (>=2.1,<2.2)", "mujoco-py (>=2.1,<2.2)"]
+other = ["matplotlib (>=3.0)", "moviepy (>=1.0.0)", "opencv-python (>=3.0)"]
+testing = ["dill (>=0.3.7)", "pytest (==7.1.3)", "scipy (>=1.7.3)"]
+torch = ["torch (>=1.0.0)"]
+toy-text = ["pygame (>=2.1.3)", "pygame (>=2.1.3)"]
+
 [[package]]
 name = "h11"
 version = "0.14.0"
@@ -2927,6 +2968,17 @@ files = [
     {file = "types_python_dateutil-2.9.0.20241003-py3-none-any.whl", hash = "sha256:250e1d8e80e7bbc3a6c99b907762711d1a1cdd00e978ad39cb5940f6f0a87f3d"},
 ]
 
+[[package]]
+name = "typing-extensions"
+version = "4.12.2"
+description = "Backported and Experimental Type Hints for Python 3.8+"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
+    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
+]
+
 [[package]]
 name = "tzdata"
 version = "2024.2"
@@ -3058,4 +3110,4 @@ updater = ["alteryx-open-src-update-checker (>=3.1.0)"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.12"
-content-hash = "14251a2aa051d0453baa081f3c5967a8fc2d57d32f379f3b899973001543c094"
+content-hash = "299cd82afa9f00a090d3ef039f4dabc019809a8a0f8b4111a3086133dec02d69"
diff --git a/pyproject.toml b/pyproject.toml
index d84d8aa..4ef6233 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,6 +14,7 @@ pandas = "^2.2.2"
 matplotlib = "^3.9.2"
 imbalanced-learn = "^0.12.3"
 featuretools = "^1.31.0"
+gymnasium = "^1.0.0"
 
 
 [build-system]