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https://github.com/ltcptgeneral/cse151b-final-project.git
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copy the wordle env locally and fix the obs return
This commit is contained in:
Arthur Lu
parent
5ec123e0f1
commit
5672169073
1
.gitignore
vendored
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.gitignore
vendored
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**/data/*
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**/*.zip
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**/
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157
dqn_wordle.ipynb
157
dqn_wordle.ipynb
@ -2,64 +2,55 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 83,
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"metadata": {},
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"outputs": [],
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"source": [
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"import gym\n",
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"import gym_wordle\n",
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"from stable_baselines3 import DQN\n",
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"from stable_baselines3 import DQN, PPO, common\n",
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"import numpy as np\n",
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"import tqdm"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 84,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<Monitor<WordleEnv instance>>\n"
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]
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}
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],
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"source": [
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"env = gym.make(\"Wordle-v0\")\n",
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"env = gym_wordle.wordle.WordleEnv()\n",
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"env = common.monitor.Monitor(env)\n",
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"\n",
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"print(env)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"execution_count": 85,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Using cuda device\n",
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"Wrapping the env in a DummyVecEnv.\n"
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]
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}
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],
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"source": [
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"total_timesteps = 100000\n",
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"model = DQN(\"MlpPolicy\", env, verbose=0)\n",
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"model.learn(total_timesteps=total_timesteps, progress_bar=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def test(model):\n",
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"\n",
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" end_rewards = []\n",
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"\n",
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" for i in range(1000):\n",
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" \n",
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" state = env.reset()\n",
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"\n",
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" done = False\n",
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"\n",
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" while not done:\n",
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"\n",
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" action, _states = model.predict(state, deterministic=True)\n",
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"\n",
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" state, reward, done, info = env.step(action)\n",
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" \n",
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" end_rewards.append(reward == 0)\n",
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" \n",
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" return np.sum(end_rewards) / len(end_rewards)"
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"total_timesteps = 1000\n",
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"model = PPO(\"MlpPolicy\", env, verbose=1)\n",
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"model.learn(total_timesteps=total_timesteps)"
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]
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},
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{
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@ -77,7 +68,93 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"model = DQN.load(\"dqn_wordle\")"
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"model = PPO.load(\"dqn_wordle\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[16 18 5 15 14 3 3 1 3 3]\n",
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" [ 0 0 0 0 0 0 0 0 0 0]\n",
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" [ 0 0 0 0 0 0 0 0 0 0]\n",
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" [ 0 0 0 0 0 0 0 0 0 0]\n",
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" [ 0 0 0 0 0 0 0 0 0 0]\n",
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" [ 0 0 0 0 0 0 0 0 0 0]] -1.0 False {}\n",
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"[[16 18 5 15 14 3 3 1 3 3]\n",
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" [16 18 5 15 14 3 3 1 3 3]\n",
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" [ 0 0 0 0 0 0 0 0 0 0]\n",
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" [ 0 0 0 0 0 0 0 0 0 0]\n",
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" [ 0 0 0 0 0 0 0 0 0 0]\n",
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" [ 0 0 0 0 0 0 0 0 0 0]] -1.0 False {}\n",
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"[[16 18 5 15 14 3 3 1 3 3]\n",
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" [16 18 5 15 14 3 3 1 3 3]\n",
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" [16 18 5 15 14 3 3 1 3 3]\n",
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" [ 0 0 0 0 0 0 0 0 0 0]\n",
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" [ 0 0 0 0 0 0 0 0 0 0]\n",
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" [ 0 0 0 0 0 0 0 0 0 0]] -1.0 False {}\n",
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"[[16 18 5 15 14 3 3 1 3 3]\n",
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" [16 18 5 15 14 3 3 1 3 3]\n",
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" [16 18 5 15 14 3 3 1 3 3]\n",
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" [16 18 5 15 14 3 3 1 3 3]\n",
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" [ 0 0 0 0 0 0 0 0 0 0]\n",
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" [ 0 0 0 0 0 0 0 0 0 0]] -1.0 False {}\n",
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"[[16 18 5 15 14 3 3 1 3 3]\n",
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" [16 18 5 15 14 3 3 1 3 3]\n",
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" [16 18 5 15 14 3 3 1 3 3]\n",
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" [16 18 5 15 14 3 3 1 3 3]\n",
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" [16 18 5 15 14 3 3 1 3 3]\n",
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" [ 0 0 0 0 0 0 0 0 0 0]] -1.0 False {}\n",
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"[[16 18 5 15 14 3 3 1 3 3]\n",
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" [16 18 5 15 14 3 3 1 3 3]\n",
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" [16 18 5 15 14 3 3 1 3 3]\n",
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" [16 18 5 15 14 3 3 1 3 3]\n",
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" [16 18 5 15 14 3 3 1 3 3]\n",
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" [16 18 5 15 14 3 3 1 3 3]] -1.0 True {}\n"
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]
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},
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{
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"ename": "KeyError",
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"evalue": "'correct'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[82], line 19\u001b[0m\n\u001b[1;32m 15\u001b[0m state, reward, done, info \u001b[38;5;241m=\u001b[39m env\u001b[38;5;241m.\u001b[39mstep(action)\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28mprint\u001b[39m(state, reward, done, info)\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43minfo\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcorrect\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m:\n\u001b[1;32m 20\u001b[0m wins \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m#end_rewards.append(reward == 0)\u001b[39;00m\n\u001b[1;32m 23\u001b[0m \n\u001b[1;32m 24\u001b[0m \u001b[38;5;66;03m#return np.sum(end_rewards) / len(end_rewards)\u001b[39;00m\n",
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"\u001b[0;31mKeyError\u001b[0m: 'correct'"
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]
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}
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],
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"source": [
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"env = gym_wordle.wordle.WordleEnv()\n",
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"\n",
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"for i in range(1):\n",
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" \n",
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" state = env.reset()\n",
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"\n",
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" done = False\n",
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"\n",
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" wins = 0\n",
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"\n",
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" while not done:\n",
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"\n",
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" action, _states = model.predict(state, deterministic=True)\n",
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"\n",
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" state, reward, done, info = env.step(action)\n",
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"\n",
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" print(state, reward, done, info)\n",
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"\n",
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" if info[\"correct\"]:\n",
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" wins += 1\n",
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" \n",
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" #end_rewards.append(reward == 0)\n",
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" \n",
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"#return np.sum(end_rewards) / len(end_rewards)\n"
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]
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},
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{
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@ -85,9 +162,7 @@
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(test(model))"
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]
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"source": []
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}
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],
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"metadata": {
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