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https://github.com/ltcptgeneral/cse151b-final-project.git
synced 2024-12-25 17:49:10 +00:00
added state saving
This commit is contained in:
Ethan Shapiro
parent
4fb81317f0
commit
3747af9d22
@ -43,6 +43,174 @@
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"check_env(env) # Optional: Verify the environment is compatible with SB3"
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"initial_state = env.clone_state()"
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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": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"obs, _ = env.reset()"
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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": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"model_save_path = \"wordle_ppo_model\"\n",
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"model = PPO.load(model_save_path)"
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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": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"action, _ = model.predict(obs)\n",
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"obs, reward, done, _, info = env.step(action)"
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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": 24,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"5"
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]
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},
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"execution_count": 24,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"action % 26"
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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": 28,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"5"
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]
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},
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"execution_count": 28,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ord('f') - ord('a')"
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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": 26,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'f'"
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]
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},
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"execution_count": 26,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chr(ord('a') + action % 26)"
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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": 16,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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" 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
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" 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
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" 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
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" 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
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" 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
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" 1, 1])"
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]
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},
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"obs"
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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": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"env.set_state(initial_state)"
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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": 20,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"False"
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]
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},
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"execution_count": 20,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"all(env.get_obs() == obs)"
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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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"# Perform your action to see the outcome\n",
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"action = # Define your action\n",
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"observation, reward, done, info = env.step(action)\n",
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"\n",
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"# Revert to the initial state\n",
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"env.env.set_state(initial_state)"
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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": 4,
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@ -128,7 +296,7 @@
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}
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],
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"source": [
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"model_save_path = \"wordle_ppo_model\"\n",
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"model_save_path = \"wordle_ppo_model_test\"\n",
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"config = {\n",
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" \"policy_type\": \"MlpPolicy\",\n",
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" \"total_timesteps\": 200_000\n",
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@ -369,7 +537,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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"version": "3.11.5"
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}
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},
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"nbformat": 4,
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@ -6,6 +6,7 @@ import numpy as np
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from stable_baselines3 import PPO, DQN
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from letter_guess import LetterGuessingEnv
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def load_valid_words(file_path='wordle_words.txt'):
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"""
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Load valid five-letter words from a specified text file.
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@ -20,8 +21,9 @@ def load_valid_words(file_path='wordle_words.txt'):
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valid_words = [line.strip() for line in file if len(line.strip()) == 5]
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return valid_words
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class AI:
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def __init__(self, vocab_file, model_file, num_letters=5, num_guesses=6, use_q_model = False):
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def __init__(self, vocab_file, model_file, num_letters=5, num_guesses=6, use_q_model=False):
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self.vocab_file = vocab_file
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self.num_letters = num_letters
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self.num_guesses = 6
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@ -36,18 +38,21 @@ class AI:
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if use_q_model:
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# we initialize the same q env as the model train ONLY to simplify storing/calculating the gym state, not used to control the game at all
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self.q_env = LetterGuessingEnv(vocab_file)
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self.q_env_state, _ = self.q_env.reset()
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# load model
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self.q_model = PPO.load(model_file)
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self.q_env_state = None
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self.reset()
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def solve_eval(self, results_callback):
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num_guesses = 0
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while [len(e) for e in self.domains] != [1 for _ in range(self.num_letters)]:
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num_guesses += 1
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# sample a word, this would use the q_env_state if the q_model is used
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word = self.sample()
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# get emulated results
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results = results_callback(word)
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if self.use_q_model:
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@ -59,7 +64,7 @@ class AI:
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self.arc_consistency(word, results)
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return num_guesses, word
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def solve(self):
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num_guesses = 0
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while [len(e) for e in self.domains] != [1 for _ in range(self.num_letters)]:
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@ -132,9 +137,31 @@ class AI:
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pattern = re.compile(regex_string)
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# From the words with the highest scores, only return the best word that match the regex pattern
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max_qval = float('-inf')
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best_word = None
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for word, _ in self.best_words:
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# reset the state back to before we guessed a word
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self.q_env.set_state(self.q_env_state)
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if pattern.match(word) and False not in [e in word for e in self.possible_letters]:
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return word
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if self.use_q_model:
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# Use policy to grade word
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# get the state and action pairs
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curr_qval = 0
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for l in word:
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action = ord(l) - ord('a')
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q_val = self.q_model.policy.evaluate_actions(self.q_env.get_obs(), action)
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_, _, _, _, _ = self.q_env.step(action)
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curr_qval += q_val
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if curr_qval > max_qval:
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max_qval = curr_qval
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best_word = word
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else:
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# otherwise return the word from eric heuristic
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return word
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self.q_env.set_state(self.q_env_state)
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return best_word
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def get_vocab(self, vocab_file):
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vocab = []
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@ -3,6 +3,7 @@ from gymnasium import spaces
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import numpy as np
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import random
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import re
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import copy
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class LetterGuessingEnv(gym.Env):
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@ -29,8 +30,28 @@ class LetterGuessingEnv(gym.Env):
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self.reset()
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def clone_state(self):
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# Clone the current state
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return {
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'target_word': self.target_word,
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'letter_flags': copy.deepcopy(self.letter_flags),
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'letter_positions': copy.deepcopy(self.letter_positions),
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'guessed_letters': copy.deepcopy(self.guessed_letters),
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'guess_prefix': self.guess_prefix,
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'round': self.round
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}
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def set_state(self, state):
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# Restore the state
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self.target_word = state['target_word']
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self.letter_flags = copy.deepcopy(state['letter_flags'])
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self.letter_positions = copy.deepcopy(state['letter_positions'])
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self.guessed_letters = copy.deepcopy(state['guessed_letters'])
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self.guess_prefix = state['guess_prefix']
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self.round = state['round']
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def step(self, action):
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letter_index = action % 26 # Assuming action is the letter index directly
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letter_index = action # Assuming action is the letter index directly
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position = len(self.guess_prefix) # The next position in the prefix is determined by its current length
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letter = chr(ord('a') + letter_index)
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@ -56,8 +77,8 @@ class LetterGuessingEnv(gym.Env):
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reward = 1 # Reward for adding new information by trying a new letter
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# Update the letter_positions matrix to reflect the new guess
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if position == 4:
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self.letter_positions[:,:] = 1
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if position == 4:
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self.letter_positions[:, :] = 1
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else:
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self.letter_positions[:, position] = 0
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self.letter_positions[letter_index, position] = 1
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@ -77,8 +98,8 @@ class LetterGuessingEnv(gym.Env):
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# reward = 5
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done = True
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obs = self._get_obs()
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obs = self.get_obs()
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if reward < -5:
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print(obs, reward, done)
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exit(0)
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@ -93,7 +114,7 @@ class LetterGuessingEnv(gym.Env):
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self.guessed_letters = set()
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self.guess_prefix = "" # Reset the guess prefix for the new episode
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self.round = 0
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return self._get_obs(), {}
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return self.get_obs(), {}
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def encode_word(self, word):
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encoded = np.zeros((26,))
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@ -102,7 +123,7 @@ class LetterGuessingEnv(gym.Env):
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encoded[index] = 1
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return encoded
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def _get_obs(self):
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def get_obs(self):
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return np.concatenate([self.letter_flags.flatten(), self.letter_positions.flatten()])
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def render(self, mode='human'):
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