diff --git a/.gitignore b/.gitignore index 3d6ff5e..5bd8281 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,6 @@ **/data/* **/*.zip **/__pycache__ -/env \ No newline at end of file +/env +**/runs/* +**/wandb/* \ No newline at end of file diff --git a/dqn_letter_gssr.ipynb b/dqn_letter_gssr.ipynb index fd92d10..1039266 100644 --- a/dqn_letter_gssr.ipynb +++ b/dqn_letter_gssr.ipynb @@ -25,31 +25,2890 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "LetterGuessingEnv.__init__() missing 1 required positional argument: 'seed'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[2], line 5\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mstable_baselines3\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommon\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01menv_checker\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m check_env\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mletter_guess\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m LetterGuessingEnv\n\u001b[1;32m----> 5\u001b[0m env \u001b[38;5;241m=\u001b[39m \u001b[43mLetterGuessingEnv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalid_words\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mload_valid_words\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Make sure to load your valid words\u001b[39;00m\n\u001b[0;32m 6\u001b[0m check_env(env) \u001b[38;5;66;03m# Optional: Verify the environment is compatible with SB3\u001b[39;00m\n\u001b[0;32m 8\u001b[0m model \u001b[38;5;241m=\u001b[39m PPO(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMlpPolicy\u001b[39m\u001b[38;5;124m\"\u001b[39m, env, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", - "\u001b[1;31mTypeError\u001b[0m: LetterGuessingEnv.__init__() missing 1 required positional argument: 'seed'" - ] - } - ], + "outputs": [], "source": [ "from stable_baselines3 import PPO # Or any other suitable RL algorithm\n", "from stable_baselines3.common.env_checker import check_env\n", "from letter_guess import LetterGuessingEnv\n", - "\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\n", - "\n", - "model = PPO(\"MlpPolicy\", env, verbose=1)\n", + "check_env(env) # Optional: Verify the environment is compatible with SB3" + ] + }, + { + "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" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\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": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Run data is saved locally in /home/art/cse151b-final-project/wandb/run-20240319_162920-ot2i0b8h" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Syncing run confused-meadow-3 to Weights & Biases (docs)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View project at https://wandb.ai/fulltime/wordle" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View run at https://wandb.ai/fulltime/wordle/runs/ot2i0b8h" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "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/ot2i0b8h/PPO_1\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "585e7545478a485aa91c487b8630840f", + "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 | 2.48 |\n", + "| ep_rew_mean | -3.7 |\n", + "| time/ | |\n", + "| fps | 465 |\n", + "| iterations | 1 |\n", + "| time_elapsed | 4 |\n", + "| total_timesteps | 2048 |\n", + "---------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 2.49 |\n", + "| ep_rew_mean | -3.65 |\n", + "| time/ | |\n", + "| fps | 395 |\n", + "| iterations | 2 |\n", + "| time_elapsed | 10 |\n", + "| total_timesteps | 4096 |\n", + "| train/ | |\n", + "| approx_kl | 0.04501068 |\n", + "| clip_fraction | 0.427 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -3.23 |\n", + "| explained_variance | 0.189 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.205 |\n", + "| n_updates | 10 |\n", + "| policy_gradient_loss | -0.0667 |\n", + "| value_loss | 0.997 |\n", + "----------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------------------------------\n", + "| rollout/ | |\n", + "| ep_len_mean | 2.84 |\n", + "| ep_rew_mean | -3.4 |\n", + "| time/ | |\n", + "| fps | 381 |\n", + "| iterations | 3 |\n", + "| time_elapsed | 16 |\n", + "| total_timesteps | 6144 |\n", + "| train/ | |\n", + "| approx_kl | 0.01765968 |\n", + "| clip_fraction | 0.319 |\n", + "| clip_range | 0.2 |\n", + "| entropy_loss | -3.17 |\n", + "| explained_variance | 0.481 |\n", + "| learning_rate | 0.0003 |\n", + "| loss | 0.123 |\n", + "| n_updates | 20 |\n", + "| policy_gradient_loss | -0.0525 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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "wandb: WARNING Source type is set to 'repo' but some required information is missing from the environment. A job will not be created from this run. See https://docs.wandb.ai/guides/launch/create-job\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "
Run history:

global_step▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███
rollout/ep_len_mean▁▁▁▁▁▁▁▁▁▂▂▂▂▂▂▂▂▂▄█▅▄▂▂▃▂▂▄▂▂▂▁▁▂▂▃▂▂▂▄
rollout/ep_rew_mean▇▇▇▇▇▇▇▇▇█████████▅▁▅▆██▇██▆███▇▇██▇███▅
time/fps█▂▂▁▁▁▁▁▁▁▁▁▁▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▃▃▃
train/approx_kl▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁▂▂▂▁▂▃▂▁▁█▁▂▃▂▁▂▂▅
train/clip_fraction█▇▇▅▅▆▅▄▆▄▂▂▂▂▃▂▂▂▁▂▄▄▅▅▆▃▂▄▂▂▂▄▂▅▃▅▅▅▂▄
train/clip_range▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
train/entropy_loss▁▁▂▃▃▄▅▅▆▆▆▇▇▇▇▇▇▇██▇▇▇▇▇▇█▇▇▇█▇▇▇▇▆▇▇█▇
train/explained_variance▃▄▅▆▆▆▆▄▆▆▅▇█▇▆██▆▁▄▄▃▇▇▄▇█▇▆▆██▅▃▆▆▆▇▇▇
train/learning_rate▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
train/loss▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▆█▂▁▁▂▁▁▁▁▁▁▁▁▁▁▂▂▃▁▁
train/policy_gradient_loss▁▂▂▄▄▄▄▅▃▄▆▇▇▆▅▆▇▄█▆▆▅▄▄▃▅▇▆▇▆█▅▇▄▆▅▆▆▆▅
train/value_loss▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃██▂▁▁▃▂▁▁▁▁▁▁▁▁▁▃▂▂▁▁

Run summary:

global_step200704
rollout/ep_len_mean24.77
rollout/ep_rew_mean-11.45
time/fps389.0
train/approx_kl0.26313
train/clip_fraction0.17793
train/clip_range0.2
train/entropy_loss-0.36315
train/explained_variance0.89819
train/learning_rate0.0003
train/loss0.24744
train/policy_gradient_loss-0.02851
train/value_loss0.52073

"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run confused-meadow-3 at: https://wandb.ai/fulltime/wordle/runs/ot2i0b8h
Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 2 other file(s)"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Find logs at: ./wandb/run-20240319_162920-ot2i0b8h/logs"
+      ],
+      "text/plain": [
+       ""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model = PPO(config[\"policy_type\"], env=env, verbose=0, tensorboard_log=f\"runs/{run.id}\")\n",
     "\n",
     "# Train for a certain number of timesteps\n",
-    "model.learn(total_timesteps=100000)\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()\n",
     "\n",
     "# Save the model\n",
     "model.save(\"wordle_ppo_model\")"
@@ -57,10 +2916,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "model.save(\"wordle_ppo_model\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = PPO.load(\"wordle_ppo_model\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1000/1000 [00:20<00:00, 49.06it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-6.703\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "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)"
+   ]
   }
  ],
  "metadata": {
@@ -79,7 +2988,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/gym_wordle/__init__.py b/gym_wordle/__init__.py
deleted file mode 100644
index 9efa1cd..0000000
--- a/gym_wordle/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from gym.envs.registration import register
-from .wordle import WordleEnv
-
-register(
-    id='Wordle-v0',
-    entry_point='gym_wordle.wordle:WordleEnv'
-)
diff --git a/gym_wordle/dictionary/guess_list.csv b/gym_wordle/dictionary/guess_list.csv
deleted file mode 100644
index 670b1bb..0000000
--- a/gym_wordle/dictionary/guess_list.csv
+++ /dev/null
@@ -1,12972 +0,0 @@
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-squab
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-squaw
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-squib
-squid
-squit
-squiz
-stabs
-stack
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\ No newline at end of file
diff --git a/gym_wordle/dictionary/guess_list.npy b/gym_wordle/dictionary/guess_list.npy
deleted file mode 100644
index c235013..0000000
Binary files a/gym_wordle/dictionary/guess_list.npy and /dev/null differ
diff --git a/gym_wordle/dictionary/solution_list.csv b/gym_wordle/dictionary/solution_list.csv
deleted file mode 100644
index 821a934..0000000
--- a/gym_wordle/dictionary/solution_list.csv
+++ /dev/null
@@ -1,2315 +0,0 @@
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-until
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-unzip
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-upset
-urban
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-usage
-usher
-using
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-usurp
-utile
-utter
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-valet
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-valor
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-valve
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-waist
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-waste
-watch
-water
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-waxen
-weary
-weave
-wedge
-weedy
-weigh
-weird
-welch
-welsh
-wench
-whack
-whale
-wharf
-wheat
-wheel
-whelp
-where
-which
-whiff
-while
-whine
-whiny
-whirl
-whisk
-white
-whole
-whoop
-whose
-widen
-wider
-widow
-width
-wield
-wight
-willy
-wimpy
-wince
-winch
-windy
-wiser
-wispy
-witch
-witty
-woken
-woman
-women
-woody
-wooer
-wooly
-woozy
-wordy
-world
-worry
-worse
-worst
-worth
-would
-wound
-woven
-wrack
-wrath
-wreak
-wreck
-wrest
-wring
-wrist
-write
-wrong
-wrote
-wrung
-wryly
-yacht
-yearn
-yeast
-yield
-young
-youth
-zebra
-zesty
-zonal
\ No newline at end of file
diff --git a/gym_wordle/dictionary/solution_list.npy b/gym_wordle/dictionary/solution_list.npy
deleted file mode 100644
index a03dd86..0000000
Binary files a/gym_wordle/dictionary/solution_list.npy and /dev/null differ
diff --git a/gym_wordle/utils.py b/gym_wordle/utils.py
deleted file mode 100644
index 2b04f1e..0000000
--- a/gym_wordle/utils.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import numpy as np
-import numpy.typing as npt
-
-from pathlib import Path
-
-
-_chars = ' abcdefghijklmnopqrstuvwxyz'
-_char_d = {c: i for i, c in enumerate(_chars)}
-
-
-def to_english(array: npt.NDArray[np.int64]) -> str:
-    """Converts a numpy integer array into a corresponding English string.
-
-    Args:
-        array: Word in array (int) form. It is assumed that each integer in the
-          array is between 0,...,26 (inclusive).
-
-    Returns:
-        A (lowercase) string representation of the word.    
-    """
-    return ''.join(_chars[i] for i in array)
-
-
-def to_array(word: str) -> npt.NDArray[np.int64]:
-    """Converts a string of characters into a corresponding numpy array.
-
-    Args:
-        word: Word in string form. It is assumed that each character in the
-          string is either an empty space ' ' or lowercase alphabetical
-          character.
-
-    Returns:
-        An array representation of the word.
-    """
-    return np.array([_char_d[c] for c in word])
-
-
-def get_words(category: str, build: bool = False) -> npt.NDArray[np.int64]:
-    """Loads a list of words in array form. 
-
-    If specified, this will recompute the list from the human-readable list of
-    words, and save the results in array form.
-
-    Args:
-        category: Either 'guess' or 'solution', which corresponds to the list
-          of acceptable guess words and the list of acceptable solution words.
-        build: If True, recomputes and saves the array-version of the computed
-          list for future access.
-
-    Returns:
-        An array representation of the list of words specified by the category.
-        This array has two dimensions, and the number of columns is fixed at
-        five.
-    """
-    assert category in {'guess', 'solution'}
-
-    arr_path = Path(__file__).parent / f'dictionary/{category}_list.npy'
-    if build:
-        list_path = Path(__file__).parent / f'dictionary/{category}_list.csv'
-
-        with open(list_path, 'r') as f:
-            words = np.array([to_array(line.strip()) for line in f])
-            np.save(arr_path, words)
-
-    return np.load(arr_path)
-
-
-def play():
-    """Play Wordle yourself!"""
-    import gym
-    import gym_wordle
-
-    env = gym.make('Wordle-v0')  # load the environment
-
-    env.reset()
-    solution = to_english(env.unwrapped.solution_space[env.solution]).upper()  # no peeking!
-
-    done = False
-
-    while not done:
-        action = -1
-
-        # in general, the environment won't be forgiving if you input an
-        # invalid word, but for this function I want to let you screw up user
-        # input without consequence, so just loops until valid input is taken
-        while not env.action_space.contains(action):
-            guess = input('Guess: ')
-            action = env.unwrapped.action_space.index_of(to_array(guess))
-
-        state, reward, done, info = env.step(action)
-        env.render()
-
-    print(f"The word was {solution}")
diff --git a/gym_wordle/wordle.py b/gym_wordle/wordle.py
deleted file mode 100644
index 9152117..0000000
--- a/gym_wordle/wordle.py
+++ /dev/null
@@ -1,353 +0,0 @@
-import gymnasium as gym
-import numpy as np
-import numpy.typing as npt
-from sty import fg, bg, ef, rs
-
-from collections import Counter
-from gym_wordle.utils import to_english, to_array, get_words
-from typing import Optional
-from collections import defaultdict
-
-
-class WordList(gym.spaces.Discrete):
-    """Super class for defining a space of valid words according to a specified
-    list.
-
-    The space is a subclass of gym.spaces.Discrete, where each element
-    corresponds to an index of a valid word in the word list. The obfuscation
-    is necessary for more direct implementation of RL algorithms, which expect
-    spaces of less sophisticated form.
-
-    In addition to the default methods of the Discrete space, it implements
-    a __getitem__ method for easy index lookup, and an index_of method to
-    convert potential words into their corresponding index (if they exist).
-    """
-
-    def __init__(self, words: npt.NDArray[np.int64], **kwargs):
-        """
-        Args:
-            words: Collection of words in array form with shape (_, 5), where
-              each word is a row of the array. Each array element is an integer
-              between 0,...,26 (inclusive).
-            kwargs: See documentation for gym.spaces.MultiDiscrete
-        """
-        super().__init__(words.shape[0], **kwargs)
-        self.words = words
-
-    def __getitem__(self, index: int) -> npt.NDArray[np.int64]:
-        """Obtains the (int-encoded) word associated with the given index.
-
-        Args:
-            index: Index for the list of words.
-
-        Returns:
-            Associated word at the position specified by index.
-        """
-        return self.words[index]
-
-    def index_of(self, word: npt.NDArray[np.int64]) -> int:
-        """Given a word, determine its index in the list (if it exists),
-        otherwise returning -1 if no index exists.
-
-        Args:
-            word: Word to find in the word list.
-
-        Returns:
-            The index of the given word if it exists, otherwise -1.
-        """
-        try:
-            index, = np.nonzero((word == self.words).all(axis=1))
-            return index[0]
-        except:
-            return -1
-
-
-class SolutionList(WordList):
-    """Space for *solution* words to the Wordle environment.
-
-    In the game Wordle, there are two different collections of words:
-
-    * "guesses", which the game accepts as valid words to use to guess the
-      answer.
-    * "solutions", which the game uses to choose solutions from.
-
-    Of course, the set of solutions is a strict subset of the set of guesses.
-
-    This class represents the set of solution words.
-    """
-
-    def __init__(self, **kwargs):
-        """
-        Args:
-            kwargs: See documentation for gym.spaces.MultiDiscrete
-        """
-        words = get_words('solution')
-        super().__init__(words, **kwargs)
-
-
-class WordleObsSpace(gym.spaces.Box):
-    """Implementation of the state (observation) space in terms of gym
-    primitives, in this case, gym.spaces.Box.
-
-    The Wordle observation space can be thought of as a 6x5 array with two
-    channels:
-
-      - the character channel, indicating which characters are placed on the
-        board (unfilled rows are marked with the empty character, 0)
-      - the flag channel, indicating the in-game information associated with
-        each character's placement (green highlight, yellow highlight, etc.)
-
-    where there are 6 rows, one for each turn in the game, and 5 columns, since
-    the solution will always be a word of length 5.
-
-    For simplicity, and compatibility with stable_baselines algorithms,
-    this multichannel is modeled as a 6x10 array, where the two channels are
-    horizontally appended (along columns). Thus each row in the observation
-    should be interpreted as c0 c1 c2 c3 c4 f0 f1 f2 f3 f4 when the word is
-    c0...c4 and its associated flags are f0...f4.
-    """
-
-    def __init__(self, **kwargs):
-        self.n_rows = 6
-        self.n_cols = 5
-        self.max_char = 26
-        self.max_flag = 4
-
-        low = np.zeros((self.n_rows, 2*self.n_cols))
-        high = np.c_[np.full((self.n_rows, self.n_cols), self.max_char),
-                     np.full((self.n_rows, self.n_cols), self.max_flag)]
-
-        super().__init__(low, high, dtype=np.int64, **kwargs)
-
-
-class GuessList(WordList):
-    """Space for *guess* words to the Wordle environment.
-
-    This class represents the set of guess words.
-    """
-
-    def __init__(self, **kwargs):
-        """
-        Args:
-            kwargs: See documentation for gym.spaces.MultiDiscrete
-        """
-        words = get_words('guess')
-        super().__init__(words, **kwargs)
-
-
-class WordleEnv(gym.Env):
-    metadata = {'render.modes': ['human']}
-
-    # Character flag codes
-    no_char = 0
-    right_pos = 1
-    wrong_pos = 2
-    wrong_char = 3
-
-    def __init__(self):
-        super().__init__()
-
-        self.action_space = GuessList()
-        self.solution_space = SolutionList()
-
-        # Example setup based on the flattened state size you're now using
-        num_position_availability = 26 * 5  # 26 letters for each of the 5 positions
-        num_global_availability = 26  # Global letter availability
-        num_letter_found_flags = 5  # One flag for each position
-        total_size = num_position_availability + num_global_availability + num_letter_found_flags
-
-        # Define the observation space to match the flattened state format
-        self.observation_space = gym.spaces.Box(low=0, high=2, shape=(total_size,), dtype=np.float32)
-
-        self._highlights = {
-            self.right_pos: (bg.green, bg.rs),
-            self.wrong_pos: (bg.yellow, bg.rs),
-            self.wrong_char: ('', ''),
-            self.no_char: ('', ''),
-        }
-
-        self.n_rounds = 6
-        self.n_letters = 5
-        self.info = {
-            'correct': False,
-            'guesses': set(),
-            'known_positions': np.full(5, -1),  # -1 for unknown, else letter index
-            'known_letters': set(),  # Letters known to be in the word
-            'not_in_word': set(),  # Letters known not to be in the word
-            'tried_positions': defaultdict(set)  # Positions tried for each letter
-        }
-        self.reset()
-
-    def _highlighter(self, char: str, flag: int) -> str:
-        """Terminal renderer functionality. Properly highlights a character
-        based on the flag associated with it.
-
-        Args:
-            char: Character in question.
-            flag: Associated flag, one of:
-                - 0: no character (render no background)
-                - 1: right position (render green background)
-                - 2: wrong position (render yellow background)
-                - 3: wrong character (render no background)
-
-        Returns:
-            Correct ASCII sequence producing the desired character in the
-            correct background.
-        """
-        front, back = self._highlights[flag]
-        return front + char + back
-
-    def reset(self, seed=None, options=None):
-        """Reset the environment to an initial state and returns an initial
-        observation.
-
-        Note: The observation space instance should be a Box space.
-
-        Returns:
-            state (object): The initial observation of the space.
-        """
-        self.round = 0
-        self.solution = self.solution_space.sample()
-        self.soln_hash = set(self.solution_space[self.solution])
-
-        self.state = {
-            'position_availability': [np.ones(26) for _ in range(5)],  # Each position can initially have any letter
-            'global_availability': np.ones(26),  # Initially, all letters are available
-            'letter_found': np.zeros(5)  # Initially, no correct letters are found
-        }
-
-        self.info = {
-            'correct': False,
-            'guesses': set(),
-            'known_positions': np.full(5, -1),
-            'known_letters': set(),
-            'not_in_word': set(),
-            'tried_positions': defaultdict(set)
-        }
-
-        self.simulate_first_guess()
-
-        return self.get_observation(), self.info
-
-    def simulate_first_guess(self):
-        fixed_first_guess = "rates"  # Example: Using 'rates' as the fixed first guess
-        # Convert the fixed guess into the appropriate format (e.g., indices of letters)
-        fixed_guess_indices = to_array(fixed_first_guess)
-        solution_indices = self.solution_space[self.solution]
-
-        for pos in range(5):  # Iterate over each position in the word
-            letter_idx = fixed_guess_indices[pos]
-            if letter_idx == solution_indices[pos]:  # Correct letter in the correct position
-                self.state['position_availability'][pos] = np.zeros(26)
-                self.state['position_availability'][pos][letter_idx] = 1
-                self.state['letter_found'][pos] = 1
-            elif letter_idx in solution_indices:  # Correct letter in the wrong position
-                self.state['position_availability'][pos][letter_idx] = 0
-                # Mark this letter as still available in other positions
-                for other_pos in range(5):
-                    if self.state['letter_found'][other_pos] == 0:  # If not already found
-                        self.state['position_availability'][other_pos][letter_idx] = 1
-            else:  # Letter not in the word
-                self.state['global_availability'][letter_idx] = 0
-                # Update all positions to reflect this letter is not in the word
-                for other_pos in range(5):
-                    self.state['position_availability'][other_pos][letter_idx] = 0
-        self.round = 1  # Increment round to reflect that first guess has been simulated
-
-    def render(self, mode: str = 'human'):
-        """Renders the Wordle environment.
-
-        Currently supported render modes:
-        - human: renders the Wordle game to the terminal.
-
-        Args:
-            mode: the mode to render with.
-        """
-        if mode == 'human':
-            for row in self.state:
-                text = ''.join(map(
-                    self._highlighter,
-                    to_english(row[:self.n_letters]).upper(),
-                    row[self.n_letters:]
-                ))
-                print(text)
-        else:
-            super().render(mode=mode)
-
-    def step(self, action):
-        assert self.action_space.contains(action), 'Invalid word!'
-
-        guessed_word = self.action_space[action]
-        solution_word = self.solution_space[self.solution]
-
-        reward = 0
-        correct_guess = np.array_equal(guessed_word, solution_word)
-
-        # Initialize flags for current guess based on the new state structure
-        current_flags = np.zeros((self.n_letters, 26))  # Replaced with a more detailed flag system
-
-        # Track newly discovered information
-        new_info = False
-
-        for i in range(self.n_letters):
-            guessed_letter = guessed_word[i] - 1
-            if guessed_letter in solution_word:
-                if guessed_letter in self.info['not_in_word']:
-                    reward -= 2  # Penalize for reusing a letter found to not be in the word
-
-                if guessed_letter == solution_word[i]:
-                    # Handle correct letter in the correct position
-                    current_flags[i, :] = 0  # Set all other letters to not possible
-                    current_flags[i, guessed_letter] = 2  # Mark this letter as correct
-                    self.info['known_positions'][i] = 1  # Update known_positions
-                    reward += 10  # Reward for correct placement
-                    new_info = True
-                else:
-                    # Correct letter, wrong position
-                    if self.info['known_positions'][i] == 0:
-                        # Only update if we haven't already found the correct letter for this position
-                        current_flags[:, guessed_letter] = 2  # Mark this letter as found in another position
-                        reward += 5
-                        new_info = True
-            else:
-                # Letter not in word
-                if guessed_letter not in self.info['not_in_word']:
-                    self.info['not_in_word'].add(guessed_letter)
-                    reward -= 2  # Penalize for guessing a letter not in the word
-                    new_info = True
-                    for pos in range(self.n_letters):
-                        # Update all positions to reflect this letter is not correct
-                        current_flags[pos, guessed_letter] = 0
-
-            # Update global letter availability based on the guess
-            for letter in range(26):
-                if letter not in guessed_word or letter in self.info['not_in_word']:
-                    self.state['global_availability'][letter] = 0
-
-        # Check if the game is over
-        done = self.round == self.n_rounds - 1 or correct_guess
-        self.info['correct'] = correct_guess
-
-        if correct_guess:
-            reward += 100  # Major reward for winning
-        elif done:
-            reward -= 50  # Penalty for losing without using new information effectively
-        elif not new_info:
-            reward -= 10  # Penalty if no new information was used in this guess
-
-        self.round += 1
-
-        return self.get_observation(), reward, done, False, self.info
-
-    def get_observation(self):
-        # Flatten the position-specific letter availability
-        position_availability_flat = np.concatenate(self.state['position_availability'])
-
-        # Global availability is already a 1D array, but ensure consistency in data handling
-        global_availability_flat = self.state['global_availability'].flatten()
-
-        # Concatenate all parts of the state into a single flat array for the DQN input
-        full_state_flat = np.concatenate(
-            [position_availability_flat, global_availability_flat, self.state['letter_found']])
-
-        return full_state_flat
diff --git a/letter_guess.py b/letter_guess.py
index 63792ea..94f5865 100644
--- a/letter_guess.py
+++ b/letter_guess.py
@@ -15,18 +15,17 @@ class LetterGuessingEnv(gym.Env):
 
     metadata = {'render_modes': ['human']}
 
-    def __init__(self, valid_words, seed):
+    def __init__(self, valid_words, seed=None):
         self.action_space = spaces.Discrete(26)
-        self.observation_space = spaces.Box(low=0, high=2, shape=(26*2 + 26*4,), dtype=np.int32)
+        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.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.round = 1
 
         self.reset()
 
@@ -47,16 +46,21 @@ class LetterGuessingEnv(gym.Env):
             self.guessed_letters.add(letter)
 
             # Update letter flags based on whether the letter is in the target word
-            if self.target_word_encoded[letter_index] == 1:
+            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
-            self.letter_positions[:, position] = 0
-            self.letter_positions[letter_index, position] = 1
+            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):
@@ -65,25 +69,30 @@ class LetterGuessingEnv(gym.Env):
 
         # 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.guess_prefix = ''
             self.round += 1
 
-        # end after 3 rounds of total guesses
-        if self.round == 3:
+        # end after 5 rounds of total guesses
+        if self.round == 2:
+            # reward = 5
             done = True
 
         obs = self._get_obs()
+        
+        if reward < -50:
+            print(obs, reward, done)
 
         return obs, reward, done, False, {}
 
-    def reset(self, seed):
+    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)) * 2
-        self.letter_positions = np.ones((26, 4))
+        # 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
-        return self._get_obs()
+        self.round = 1
+        return self._get_obs(), {}
 
     def encode_word(self, word):
         encoded = np.zeros((26,))