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copy the wordle env locally and fix the obs return

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This commit is contained in:
Arthur Lu 2024-03-14 14:49:17 -07:00
parent 5ec123e0f1
commit 5672169073
2 changed files with 118 additions and 42 deletions
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3
.gitignore vendored
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@ -1,2 +1,3 @@
**/data/* **/data/*
**/*.zip **/*.zip
**/

157
dqn_wordle.ipynb
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@ -2,64 +2,55 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 83,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"import gym\n", "import gym\n",
"import gym_wordle\n", "import gym_wordle\n",
"from stable_baselines3 import DQN\n", "from stable_baselines3 import DQN, PPO, common\n",
"import numpy as np\n", "import numpy as np\n",
"import tqdm" "import tqdm"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 84,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<Monitor<WordleEnv instance>>\n"
]
}
],
"source": [ "source": [
"env = gym.make(\"Wordle-v0\")\n", "env = gym_wordle.wordle.WordleEnv()\n",
"env = common.monitor.Monitor(env)\n",
"\n", "\n",
"print(env)" "print(env)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 35, "execution_count": 85,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using cuda device\n",
"Wrapping the env in a DummyVecEnv.\n"
]
}
],
"source": [ "source": [
"total_timesteps = 100000\n", "total_timesteps = 1000\n",
"model = DQN(\"MlpPolicy\", env, verbose=0)\n", "model = PPO(\"MlpPolicy\", env, verbose=1)\n",
"model.learn(total_timesteps=total_timesteps, progress_bar=True)" "model.learn(total_timesteps=total_timesteps)"
]
},
{
"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)"
] ]
}, },
{ {
@ -77,7 +68,93 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"model = DQN.load(\"dqn_wordle\")" "model = PPO.load(\"dqn_wordle\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[16 18 5 15 14 3 3 1 3 3]\n",
" [ 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0]] -1.0 False {}\n",
"[[16 18 5 15 14 3 3 1 3 3]\n",
" [16 18 5 15 14 3 3 1 3 3]\n",
" [ 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0]] -1.0 False {}\n",
"[[16 18 5 15 14 3 3 1 3 3]\n",
" [16 18 5 15 14 3 3 1 3 3]\n",
" [16 18 5 15 14 3 3 1 3 3]\n",
" [ 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0]] -1.0 False {}\n",
"[[16 18 5 15 14 3 3 1 3 3]\n",
" [16 18 5 15 14 3 3 1 3 3]\n",
" [16 18 5 15 14 3 3 1 3 3]\n",
" [16 18 5 15 14 3 3 1 3 3]\n",
" [ 0 0 0 0 0 0 0 0 0 0]\n",
" [ 0 0 0 0 0 0 0 0 0 0]] -1.0 False {}\n",
"[[16 18 5 15 14 3 3 1 3 3]\n",
" [16 18 5 15 14 3 3 1 3 3]\n",
" [16 18 5 15 14 3 3 1 3 3]\n",
" [16 18 5 15 14 3 3 1 3 3]\n",
" [16 18 5 15 14 3 3 1 3 3]\n",
" [ 0 0 0 0 0 0 0 0 0 0]] -1.0 False {}\n",
"[[16 18 5 15 14 3 3 1 3 3]\n",
" [16 18 5 15 14 3 3 1 3 3]\n",
" [16 18 5 15 14 3 3 1 3 3]\n",
" [16 18 5 15 14 3 3 1 3 3]\n",
" [16 18 5 15 14 3 3 1 3 3]\n",
" [16 18 5 15 14 3 3 1 3 3]] -1.0 True {}\n"
]
},
{
"ename": "KeyError",
"evalue": "'correct'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"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",
"\u001b[0;31mKeyError\u001b[0m: 'correct'"
]
}
],
"source": [
"env = gym_wordle.wordle.WordleEnv()\n",
"\n",
"for i in range(1):\n",
" \n",
" state = env.reset()\n",
"\n",
" done = False\n",
"\n",
" wins = 0\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",
" print(state, reward, done, info)\n",
"\n",
" if info[\"correct\"]:\n",
" wins += 1\n",
" \n",
" #end_rewards.append(reward == 0)\n",
" \n",
"#return np.sum(end_rewards) / len(end_rewards)\n"
] ]
}, },
{ {
@ -85,9 +162,7 @@
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": []
"print(test(model))"
]
} }
], ],
"metadata": { "metadata": {