add ppo model, add cpu and cuda device support

.gitignore

@@ -1,2 +1,6 @@
 **/data/*
-**/*.zip
+**/__pycache__
+/env
+**/runs/*
+**/wandb/*
+**/models/*

dqn_letter_gssr.ipynb

@@ -0,0 +1,545 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_valid_words(file_path='wordle_words.txt'):\n",
+    "    \"\"\"\n",
+    "    Load valid five-letter words from a specified text file.\n",
+    "\n",
+    "    Parameters:\n",
+    "    - file_path (str): The path to the text file containing valid words.\n",
+    "\n",
+    "    Returns:\n",
+    "    - list[str]: A list of valid words loaded from the file.\n",
+    "    \"\"\"\n",
+    "    with open(file_path, 'r') as file:\n",
+    "        valid_words = [line.strip() for line in file if len(line.strip()) == 5]\n",
+    "    return valid_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stable_baselines3 import PPO, DQN  # Or any other suitable RL algorithm\n",
+    "from stable_baselines3.common.env_checker import check_env\n",
+    "from letter_guess import LetterGuessingEnv\n",
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "env = LetterGuessingEnv(valid_words=load_valid_words())  # Make sure to load your valid words\n",
+    "check_env(env)  # Optional: Verify the environment is compatible with SB3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "initial_state = env.clone_state()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obs, _ = env.reset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_save_path = \"wordle_ppo_model\"\n",
+    "model = PPO.load(model_save_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "action, _ = model.predict(obs)\n",
+    "obs, reward, done, _, info = env.step(action)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "action % 26"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ord('f') - ord('a')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'f'"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chr(ord('a') + action % 26)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
+       "       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
+       "       0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
+       "       1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
+       "       0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
+       "       1, 1])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "env.set_state(initial_state)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all(env.get_obs() == obs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Perform your action to see the outcome\n",
+    "action = # Define your action\n",
+    "observation, reward, done, info = env.step(action)\n",
+    "\n",
+    "# Revert to the initial state\n",
+    "env.env.set_state(initial_state)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import wandb\n",
+    "from wandb.integration.sb3 import WandbCallback"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mltcptgeneral\u001b[0m (\u001b[33mfulltime\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "Tracking run with wandb version 0.16.4"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>/home/art/cse151b-final-project/wandb/run-20240319_211220-cyh5nscz</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Syncing run <strong><a href='https://wandb.ai/fulltime/wordle/runs/cyh5nscz' target=\"_blank\">distinctive-flower-20</a></strong> to <a href='https://wandb.ai/fulltime/wordle' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View project at <a href='https://wandb.ai/fulltime/wordle' target=\"_blank\">https://wandb.ai/fulltime/wordle</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run at <a href='https://wandb.ai/fulltime/wordle/runs/cyh5nscz' target=\"_blank\">https://wandb.ai/fulltime/wordle/runs/cyh5nscz</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model_save_path = \"wordle_ppo_model_test\"\n",
+    "config = {\n",
+    "    \"policy_type\": \"MlpPolicy\",\n",
+    "    \"total_timesteps\": 200_000\n",
+    "}\n",
+    "run = wandb.init(\n",
+    "    project=\"wordle\",\n",
+    "    config=config,\n",
+    "    sync_tensorboard=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using cuda device\n",
+      "Wrapping the env with a `Monitor` wrapper\n",
+      "Wrapping the env in a DummyVecEnv.\n",
+      "Logging to runs/cyh5nscz/PPO_1\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca60c274a90b4dddaf275fe164012f16",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---------------------------------\n",
+      "| rollout/           |          |\n",
+      "|    ep_len_mean     | 2.54     |\n",
+      "|    ep_rew_mean     | -3.66    |\n",
+      "| time/              |          |\n",
+      "|    fps             | 721      |\n",
+      "|    iterations      | 1        |\n",
+      "|    time_elapsed    | 2        |\n",
+      "|    total_timesteps | 2048     |\n",
+      "---------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------\n",
+      "| rollout/                |             |\n",
+      "|    ep_len_mean          | 2.53        |\n",
+      "|    ep_rew_mean          | -3.61       |\n",
+      "| time/                   |             |\n",
+      "|    fps                  | 718         |\n",
+      "|    iterations           | 2           |\n",
+      "|    time_elapsed         | 5           |\n",
+      "|    total_timesteps      | 4096        |\n",
+      "| train/                  |             |\n",
+      "|    approx_kl            | 0.011673957 |\n",
+      "|    clip_fraction        | 0.0292      |\n",
+      "|    clip_range           | 0.2         |\n",
+      "|    entropy_loss         | -3.25       |\n",
+      "|    explained_variance   | -0.126      |\n",
+      "|    learning_rate        | 0.0003      |\n",
+      "|    loss                 | 0.576       |\n",
+      "|    n_updates            | 10          |\n",
+      "|    policy_gradient_loss | -0.0197     |\n",
+      "|    value_loss           | 3.58        |\n",
+      "-----------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------\n",
+      "| rollout/                |             |\n",
+      "|    ep_len_mean          | 2.7         |\n",
+      "|    ep_rew_mean          | -3.56       |\n",
+      "| time/                   |             |\n",
+      "|    fps                  | 698         |\n",
+      "|    iterations           | 3           |\n",
+      "|    time_elapsed         | 8           |\n",
+      "|    total_timesteps      | 6144        |\n",
+      "| train/                  |             |\n",
+      "|    approx_kl            | 0.019258872 |\n",
+      "|    clip_fraction        | 0.198       |\n",
+      "|    clip_range           | 0.2         |\n",
+      "|    entropy_loss         | -3.22       |\n",
+      "|    explained_variance   | -0.211      |\n",
+      "|    learning_rate        | 0.0003      |\n",
+      "|    loss                 | 0.187       |\n",
+      "|    n_updates            | 20          |\n",
+      "|    policy_gradient_loss | -0.0215     |\n",
+      "|    value_loss           | 0.637       |\n",
+      "-----------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------\n",
+      "| rollout/                |             |\n",
+      "|    ep_len_mean          | 2.73        |\n",
+      "|    ep_rew_mean          | -3.43       |\n",
+      "| time/                   |             |\n",
+      "|    fps                  | 681         |\n",
+      "|    iterations           | 4           |\n",
+      "|    time_elapsed         | 12          |\n",
+      "|    total_timesteps      | 8192        |\n",
+      "| train/                  |             |\n",
+      "|    approx_kl            | 0.021500897 |\n",
+      "|    clip_fraction        | 0.171       |\n",
+      "|    clip_range           | 0.2         |\n",
+      "|    entropy_loss         | -3.17       |\n",
+      "|    explained_variance   | 0.378       |\n",
+      "|    learning_rate        | 0.0003      |\n",
+      "|    loss                 | 0.185       |\n",
+      "|    n_updates            | 30          |\n",
+      "|    policy_gradient_loss | -0.0214     |\n",
+      "|    value_loss           | 0.479       |\n",
+      "-----------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------\n",
+      "| rollout/                |             |\n",
+      "|    ep_len_mean          | 2.92        |\n",
+      "|    ep_rew_mean          | -3.36       |\n",
+      "| time/                   |             |\n",
+      "|    fps                  | 682         |\n",
+      "|    iterations           | 5           |\n",
+      "|    time_elapsed         | 14          |\n",
+      "|    total_timesteps      | 10240       |\n",
+      "| train/                  |             |\n",
+      "|    approx_kl            | 0.018113121 |\n",
+      "|    clip_fraction        | 0.101       |\n",
+      "|    clip_range           | 0.2         |\n",
+      "|    entropy_loss         | -3.11       |\n",
+      "|    explained_variance   | 0.448       |\n",
+      "|    learning_rate        | 0.0003      |\n",
+      "|    loss                 | 0.203       |\n",
+      "|    n_updates            | 40          |\n",
+      "|    policy_gradient_loss | -0.0183     |\n",
+      "|    value_loss           | 0.455       |\n",
+      "-----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = PPO(config[\"policy_type\"], env=env, verbose=2, tensorboard_log=f\"runs/{run.id}\", batch_size=64)\n",
+    "\n",
+    "# Train for a certain number of timesteps\n",
+    "model.learn(\n",
+    "    total_timesteps=config[\"total_timesteps\"],\n",
+    "    callback=WandbCallback(\n",
+    "        model_save_path=f\"models/{run.id}\",\n",
+    "        verbose=2,\n",
+    "    ),\n",
+    "\tprogress_bar=True\n",
+    ")\n",
+    "\n",
+    "run.finish()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.save(model_save_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = PPO.load(model_save_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rewards = 0\n",
+    "for i in tqdm(range(1000)):\n",
+    "    obs, _ = env.reset()\n",
+    "    done = False\n",
+    "    while not done:\n",
+    "        action, _ = model.predict(obs)\n",
+    "        obs, reward, done, _, info = env.step(action)\n",
+    "        rewards += reward\n",
+    "print(rewards / 1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

dqn_wordle.ipynb

@@ -1,114 +0,0 @@
-{
- "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))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

eric_wordle/.gitignore

@@ -0,0 +1,129 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

eric_wordle/README.md

@@ -0,0 +1,11 @@
+# N-dle Solver
+
+A solver designed to beat New York Time's Wordle (link [here](https://www.nytimes.com/games/wordle/index.html)). If you are bored enough, can extend to solve the more general N-dle problem (for quordle, octordle, etc.)
+
+I originally made this out of frustration for the game (and my own lack of lingual talent). One day, my friend thought she could beat my bot. To her dismay, she learned that she is no better than a machine. Let's see if you can do any better (the average number of attempts is 3.6).
+
+## Usage:
+1. Run `python main.py --n 1`
+2. Follow the prompts
+
+Currently only supports solving for 1 word at a time (i.e. wordle).

eric_wordle/ai.py

@@ -0,0 +1,206 @@
+import re
+import string
+
+import numpy as np
+
+from stable_baselines3 import PPO, DQN
+from letter_guess import LetterGuessingEnv
+import torch
+
+def load_valid_words(file_path='wordle_words.txt'):
+    """
+    Load valid five-letter words from a specified text file.
+
+    Parameters:
+    - file_path (str): The path to the text file containing valid words.
+
+    Returns:
+    - list[str]: A list of valid words loaded from the file.
+    """
+    with open(file_path, 'r') as file:
+        valid_words = [line.strip() for line in file if len(line.strip()) == 5]
+    return valid_words
+
+
+class AI:
+    def __init__(self, vocab_file, model_file, num_letters=5, num_guesses=6, use_q_model=False, device="cuda"):
+        self.device = device
+        self.vocab_file = vocab_file
+        self.num_letters = num_letters
+        self.num_guesses = 6
+
+        self.vocab, self.vocab_scores, self.letter_scores = self.get_vocab(self.vocab_file)
+        self.best_words = sorted(list(self.vocab_scores.items()), key=lambda tup: tup[1])[::-1]
+
+        self.domains = None
+        self.possible_letters = None
+
+        self.use_q_model = use_q_model
+        if use_q_model:
+            # 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
+            self.q_env = LetterGuessingEnv(load_valid_words(vocab_file))
+            self.q_env_state, _ = self.q_env.reset()
+
+            # load model
+            self.q_model = PPO.load(model_file, device=self.device)
+
+        self.reset("")
+
+    def solve_eval(self, results_callback):
+        num_guesses = 0
+        while [len(e) for e in self.domains] != [1 for _ in range(self.num_letters)]:
+            num_guesses += 1
+            if self.use_q_model:
+                self.freeze_state = self.q_env.clone_state()
+
+            # sample a word, this would use the q_env_state if the q_model is used
+            word = self.sample(num_guesses)
+
+            # get emulated results
+            results = results_callback(word)
+            if self.use_q_model:
+                self.q_env.set_state(self.freeze_state)
+                # step the q_env to match the guess we just made
+                for i in range(len(word)):
+                    char = word[i]
+                    action = ord(char) - ord('a')
+                    self.q_env_state, _, _, _, _ = self.q_env.step(action)
+
+            self.arc_consistency(word, results)
+        return num_guesses, word
+
+    def solve(self):
+        num_guesses = 0
+        while [len(e) for e in self.domains] != [1 for _ in range(self.num_letters)]:
+            num_guesses += 1
+            if self.use_q_model:
+                self.freeze_state = self.q_env.clone_state()
+
+            # sample a word, this would use the q_env_state if the q_model is used
+            word = self.sample(num_guesses)
+
+            print('-----------------------------------------------')
+            print(f'Guess #{num_guesses}/{self.num_guesses}: {word}')
+            print('-----------------------------------------------')
+
+            print(f'Performing arc consistency check on {word}...')
+            print(f'Specify 0 for completely nonexistent letter at the specified index, 1 for existent letter but incorrect index, and 2 for correct letter at correct index.')
+            results = []
+
+            # Collect results
+            for l in word:
+                while True:
+                    result = input(f'{l}: ')
+                    if result not in ['0', '1', '2']:
+                        print('Incorrect option. Try again.')
+                        continue
+                    results.append(result)
+                    break
+
+            if self.use_q_model:
+                self.q_env.set_state(self.freeze_state)
+                # step the q_env to match the guess we just made
+                for i in range(len(word)):
+                    char = word[i]
+                    action = ord(char) - ord('a')
+                    self.q_env_state, _, _, _, _ = self.q_env.step(action)
+
+            self.arc_consistency(word, results)
+        return num_guesses, word
+
+    def arc_consistency(self, word, results):
+        self.possible_letters += [word[i] for i in range(len(word)) if results[i] == '1']
+
+        for i in range(len(word)):
+            if results[i] == '0':
+                if word[i] in self.possible_letters:
+                    if word[i] in self.domains[i]:
+                        self.domains[i].remove(word[i])
+                else:
+                    for j in range(len(self.domains)):
+                        if word[i] in self.domains[j] and len(self.domains[j]) > 1:
+                            self.domains[j].remove(word[i])
+            if results[i] == '1':
+                if word[i] in self.domains[i]:
+                    self.domains[i].remove(word[i])
+            if results[i] == '2':
+                self.domains[i] = [word[i]]
+
+    def reset(self, target_word):
+        self.domains = [list(string.ascii_lowercase) for _ in range(self.num_letters)]
+        self.possible_letters = []
+
+        if self.use_q_model:
+            self.q_env_state, _ = self.q_env.reset()
+            self.q_env.target_word = target_word
+
+    def sample(self, num_guesses):
+        """
+        Samples a best word given the current domains
+        :return:
+        """
+        # Compile a regex of possible words with the current domain
+        regex_string = ''
+        for domain in self.domains:
+            regex_string += ''.join(['[', ''.join(domain), ']', '{1}'])
+        pattern = re.compile(regex_string)
+
+        # From the words with the highest scores, only return the best word that match the regex pattern
+        max_qval = float('-inf')
+        best_word = None
+        for word, _ in self.best_words:
+            # reset the state back to before we guessed a word
+            if pattern.match(word) and False not in [e in word for e in self.possible_letters]:
+                if self.use_q_model and num_guesses == 3:
+                    self.q_env.set_state(self.freeze_state)
+                    # Use policy to grade word
+                    # get the state and action pairs
+                    curr_qval = 0
+
+                    for l in word:
+                        action = ord(l) - ord('a')
+                        q_val, _, _ = self.q_model.policy.evaluate_actions(self.q_model.policy.obs_to_tensor(self.q_env.get_obs())[0], torch.Tensor(np.array([action])).to(self.device))
+                        _, _, _, _, _ = self.q_env.step(action)
+                        curr_qval += q_val
+
+                    if curr_qval > max_qval:
+                        max_qval = curr_qval
+                        best_word = word
+                else:
+                    # otherwise return the word from eric heuristic
+                    return word
+        return best_word
+
+    def get_vocab(self, vocab_file):
+        vocab = []
+        with open(vocab_file, 'r') as f:
+            for l in f:
+                vocab.append(l.strip())
+
+        # Count letter frequencies at each index
+        letter_freqs = [{letter: 0 for letter in string.ascii_lowercase} for _ in range(self.num_letters)]
+        for word in vocab:
+            for i, l in enumerate(word):
+                letter_freqs[i][l] += 1
+
+        # Assign a score to each letter at each index by the probability of it appearing
+        letter_scores = [{letter: 0 for letter in string.ascii_lowercase} for _ in range(self.num_letters)]
+        for i in range(len(letter_scores)):
+            max_freq = np.max(list(letter_freqs[i].values()))
+            for l in letter_scores[i].keys():
+                letter_scores[i][l] = letter_freqs[i][l] / max_freq
+
+        # Find a sorted list of words ranked by sum of letter scores
+        vocab_scores = {}  # (score, word)
+        for word in vocab:
+            score = 0
+            for i, l in enumerate(word):
+                score += letter_scores[i][l]
+
+            # # Optimization: If repeating letters, deduct a couple points
+            # if len(set(word)) < len(word):
+            #     score -= 0.25 * (len(word) - len(set(word)))
+
+            vocab_scores[word] = score
+
+        return vocab, vocab_scores, letter_scores

eric_wordle/dist.py

@@ -0,0 +1,37 @@
+import string
+
+import numpy as np
+
+words = []
+with open('words.txt', 'r') as f:
+    for l in f:
+        words.append(l.strip())
+
+# Count letter frequencies at each index
+letter_freqs = [{letter: 0 for letter in string.ascii_lowercase} for _ in range(5)]
+for word in words:
+    for i, l in enumerate(word):
+        letter_freqs[i][l] += 1
+
+# Assign a score to each letter at each index by the probability of it appearing
+letter_scores = [{letter: 0 for letter in string.ascii_lowercase} for _ in range(5)]
+for i in range(len(letter_scores)):
+    max_freq = np.max(list(letter_freqs[i].values()))
+    for l in letter_scores[i].keys():
+        letter_scores[i][l] = letter_freqs[i][l] / max_freq
+
+# Find a sorted list of words ranked by sum of letter scores
+word_scores = []  # (score, word)
+for word in words:
+    score = 0
+    for i, l in enumerate(word):
+        score += letter_scores[i][l]
+    word_scores.append((score, word))
+
+sorted_by_second = sorted(word_scores, key=lambda tup: tup[0])[::-1]
+print(sorted_by_second[:10])
+
+for i, (score, word) in enumerate(sorted_by_second):
+    if word == 'soare':
+        print(f'{word} with a score of {score} is found at index {i}')
+

eric_wordle/eval.py

@@ -0,0 +1,63 @@
+import argparse
+from ai import AI
+import numpy as np
+from tqdm import tqdm
+
+global solution
+
+def result_callback(word):
+
+    global solution
+
+    result = ['0', '0', '0', '0', '0']
+
+    for i, letter in enumerate(word):
+
+        if solution[i] == word[i]:
+            result[i] = '2'
+        elif letter in solution:
+            result[i] = '1'
+        else:
+            pass
+
+    return result
+
+def main(args):
+    global solution 
+
+    if args.n is None:
+        raise Exception('Need to specify n (i.e. n = 1 for wordle, n = 4 for quordle, n = 16 for sedecordle).')
+
+    ai = AI(args.vocab_file, args.model_file, use_q_model=args.q_model, device=args.device)
+
+    total_guesses = 0
+    wins = 0
+    num_eval = args.num_eval
+
+    np.random.seed(0)
+
+    for i in tqdm(range(num_eval)):
+        idx = np.random.choice(range(len(ai.vocab)))
+        solution = ai.vocab[idx]
+
+        ai.reset(solution)
+
+        guesses, word = ai.solve_eval(results_callback=result_callback)
+        if word != solution:
+            total_guesses += 5
+        else:
+            total_guesses += guesses
+            wins += 1
+
+    print(f"q_model?: {args.q_model} \t average guesses per game: {total_guesses / num_eval} \t win rate: {wins / num_eval}")
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--n', dest='n', type=int, default=None)
+    parser.add_argument('--vocab_file', dest='vocab_file', type=str, default='wordle_words.txt')
+    parser.add_argument('--num_eval', dest="num_eval", type=int, default=1000)
+    parser.add_argument('--model_file', dest="model_file", type=str, default='wordle_ppo_model')
+    parser.add_argument('--q_model', dest="q_model", type=bool, default=False)
+    parser.add_argument('--device', dest="device", type=str, default="cuda")
+    args = parser.parse_args()
+    main(args)

eric_wordle/letter_guess.py

@@ -0,0 +1 @@
+../letter_guess.py

eric_wordle/main.py

@@ -0,0 +1,22 @@
+import argparse
+from ai import AI
+
+
+def main(args):
+    if args.n is None:
+        raise Exception('Need to specify n (i.e. n = 1 for wordle, n = 4 for quordle, n = 16 for sedecordle).')
+    print(f"using q model? {args.q_model}")
+    ai = AI(args.vocab_file, args.model_file, use_q_model=args.q_model, device=args.device)
+    ai.reset("lingo")
+    ai.solve()
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--n', dest='n', type=int, default=None)
+    parser.add_argument('--vocab_file', dest='vocab_file', type=str, default='wordle_words.txt')
+    parser.add_argument('--model_file', dest="model_file", type=str, default='wordle_ppo_model')
+    parser.add_argument('--q_model', dest="q_model", type=bool, default=False)
+    parser.add_argument('--device', dest="device", type=str, default="cuda")
+    args = parser.parse_args()
+    main(args)

eric_wordle/process.py

@@ -0,0 +1,15 @@
+import pandas
+
+print('Loading in words dictionary; this may take a while...')
+df = pandas.read_json('words_dictionary.json')
+print('Done loading words dictionary.')
+words = []
+for word in df.axes[0].tolist():
+    if len(word) != 5:
+        continue
+    words.append(word)
+words.sort()
+
+with open('words.txt', 'w') as f:
+    for word in words:
+        f.write(word + '\n')

eval.sh

@@ -0,0 +1,2 @@
+python eric_wordle/eval.py --n 1 --vocab_file wordle_words.txt  --num_eval 5000
+python eric_wordle/eval.py --n 1 --vocab_file wordle_words.txt  --num_eval 5000 --q_model True --model_file wordle_ppo_model

inference.sh

@@ -0,0 +1 @@
+python eric_wordle/main.py --n 1 --vocab_file wordle_words.txt  --q_model True --model_file wordle_ppo_model --device cpu

letter_guess.py

@@ -0,0 +1,130 @@
+import gymnasium as gym
+from gymnasium import spaces
+import numpy as np
+import random
+import re
+import copy
+
+
+class LetterGuessingEnv(gym.Env):
+    """
+    Custom Gymnasium environment for a letter guessing game with a focus on forming
+    valid prefixes and words from a list of valid Wordle words. The environment tracks
+    the current guess prefix and validates it against known valid words, ending the game
+    early with a negative reward for invalid prefixes.
+    """
+
+    metadata = {'render_modes': ['human']}
+
+    def __init__(self, valid_words, seed=None):
+        self.action_space = spaces.Discrete(26)
+        self.observation_space = spaces.Box(low=0, high=1, shape=(26*2 + 26*4,), dtype=np.int32)
+
+        self.valid_words = valid_words  # List of valid Wordle words
+        self.target_word = ''  # Target word for the current episode
+        self.valid_words_str = ' '.join(self.valid_words) + ' '
+        self.letter_flags = None
+        self.letter_positions = None
+        self.guessed_letters = set()
+        self.guess_prefix = ""  # Tracks the current guess prefix
+
+        self.reset()
+
+    def clone_state(self):
+        # Clone the current state
+        return {
+            'target_word': self.target_word,
+            'letter_flags': copy.deepcopy(self.letter_flags),
+            'letter_positions': copy.deepcopy(self.letter_positions),
+            'guessed_letters': copy.deepcopy(self.guessed_letters),
+            'guess_prefix': self.guess_prefix,
+            'round': self.round
+        }
+
+    def set_state(self, state):
+        # Restore the state
+        self.target_word = state['target_word']
+        self.letter_flags = copy.deepcopy(state['letter_flags'])
+        self.letter_positions = copy.deepcopy(state['letter_positions'])
+        self.guessed_letters = copy.deepcopy(state['guessed_letters'])
+        self.guess_prefix = state['guess_prefix']
+        self.round = state['round']
+
+    def step(self, action):
+        letter_index = action  # Assuming action is the letter index directly
+        position = len(self.guess_prefix)  # The next position in the prefix is determined by its current length
+        letter = chr(ord('a') + letter_index)
+
+        reward = 0
+        done = False
+
+        # Check if the letter has already been used in the guess prefix
+        if letter in self.guessed_letters:
+            reward = -1  # Penalize for repeating letters in the prefix
+        else:
+            # Add the new letter to the prefix and update guessed letters set
+            self.guess_prefix += letter
+            self.guessed_letters.add(letter)
+
+            # Update letter flags based on whether the letter is in the target word
+            if self.target_word[position] == letter:
+                self.letter_flags[letter_index, :] = [1, 0]  # Update flag for correct guess
+            elif letter in self.target_word:
+                self.letter_flags[letter_index, :] = [0, 1]  # Update flag for correct guess wrong position
+            else:
+                self.letter_flags[letter_index, :] = [0, 0]  # Update flag for incorrect guess
+
+            reward = 1  # Reward for adding new information by trying a new letter
+
+            # Update the letter_positions matrix to reflect the new guess
+            if position == 4:
+                self.letter_positions[:, :] = 1
+            else:
+                self.letter_positions[:, position] = 0
+                self.letter_positions[letter_index, position] = 1
+
+        # Use regex to check if the current prefix can lead to a valid word
+        if not re.search(r'\b' + self.guess_prefix, self.valid_words_str):
+            reward = -5  # Penalize for forming an invalid prefix
+            done = True  # End the episode if the prefix is invalid
+
+        # guessed a full word so we reset our guess prefix to guess next round
+        if len(self.guess_prefix) == len(self.target_word):
+            self.guess_prefix = ''
+            self.round += 1
+
+        # end after 3 rounds of total guesses
+        if self.round == 3:
+            # reward = 5
+            done = True
+
+        obs = self.get_obs()
+
+        if reward < -5:
+            print(obs, reward, done)
+            exit(0)
+
+        return obs, reward, done, False, {}
+
+    def reset(self, seed=None):
+        self.target_word = random.choice(self.valid_words)
+        # self.target_word_encoded = self.encode_word(self.target_word)
+        self.letter_flags = np.ones((26, 2), dtype=np.int32)
+        self.letter_positions = np.ones((26, 4), dtype=np.int32)
+        self.guessed_letters = set()
+        self.guess_prefix = ""  # Reset the guess prefix for the new episode
+        self.round = 0
+        return self.get_obs(), {}
+
+    def encode_word(self, word):
+        encoded = np.zeros((26,))
+        for char in word:
+            index = ord(char) - ord('a')
+            encoded[index] = 1
+        return encoded
+
+    def get_obs(self):
+        return np.concatenate([self.letter_flags.flatten(), self.letter_positions.flatten()])
+
+    def render(self, mode='human'):
+        pass  # Optional: Implement rendering logic if needed