{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "import gym_wordle\n",
    "from stable_baselines3 import DQN\n",
    "import numpy as np\n",
    "import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = gym.make(\"Wordle-v0\")\n",
    "\n",
    "print(env)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_timesteps = 100000\n",
    "model = DQN(\"MlpPolicy\", env, verbose=0)\n",
    "model.learn(total_timesteps=total_timesteps, progress_bar=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(model):\n",
    "\n",
    "    end_rewards = []\n",
    "\n",
    "    for i in range(1000):\n",
    "        \n",
    "        state = env.reset()\n",
    "\n",
    "        done = False\n",
    "\n",
    "        while not done:\n",
    "\n",
    "            action, _states = model.predict(state, deterministic=True)\n",
    "\n",
    "            state, reward, done, info = env.step(action)\n",
    "            \n",
    "        end_rewards.append(reward == 0)\n",
    "        \n",
    "    return np.sum(end_rewards) / len(end_rewards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"dqn_wordle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DQN.load(\"dqn_wordle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(test(model))"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
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