try more time steps

dqn_wordle.py

@@ -0,0 +1,38 @@
+import gym
+import sys
+from stable_baselines3 import DQN
+from stable_baselines3.common.env_util import make_vec_env
+import wordle_gym
+import numpy as np
+from tqdm import tqdm
+
+def train (model, env, total_timesteps = 100000): 
+    model.learn(total_timesteps=total_timesteps, progress_bar=True)
+    model.save("dqn_wordle")
+
+def test(model, env, test_num=1000):
+
+    total_correct = 0
+
+    for i in tqdm(range(test_num)):
+
+        model = DQN.load("dqn_wordle")
+
+        env = gym.make("wordle-v0")
+        obs = env.reset()
+        done = False
+        while not done:
+            action, _states = model.predict(obs)
+            obs, rewards, done, info = env.step(action)
+            
+    return total_correct / test_num
+
+if __name__ == "__main__":
+    
+    env = gym.make("wordle-v0")
+    model = DQN("MlpPolicy", env, verbose=0)
+    print(env)
+    print(model)
+
+    train(model, env, total_timesteps=500000)
+    print(test(model, env))

gym_wordle/__init__.py

@@ -1,7 +0,0 @@
-from gym.envs.registration import register
-from .wordle import WordleEnv
-
-register(
-    id='Wordle-v0',
-    entry_point='gym_wordle.wordle:WordleEnv'
-)

gym_wordle/utils.py

@@ -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}")

gym_wordle/wordle.py

@@ -1,296 +0,0 @@
-import 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 
-
-class WordList(gym.spaces.Discrete):
-    """Super class for defining a space of valid words according to a specified
-    list.
-
-    TODO: Fix these paragraphs
-    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.
-
-    Reference: https://fivethirtyeight.com/features/when-the-riddler-met-wordle/
-
-    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
-    primatives, 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 the 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.
-
-    While the superclass method `sample` is available to the WordleObsSpace, it
-    should be emphasized that the output of `sample` will (almost surely) not
-    correspond to a real game configuration, because the sampling is not out of
-    possible game configurations. Instead, the Box superclass just samples the
-    integer array space uniformly.
-    """
-
-    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 *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.
-
-    Reference: https://fivethirtyeight.com/features/when-the-riddler-met-wordle/
-
-    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.seed()
-        self.action_space = GuessList()
-        self.solution_space = SolutionList()
-
-        self.observation_space = WordleObsSpace()
-
-        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
-
-    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):
-        self.round = 0
-        self.solution = self.solution_space.sample()
-
-        self.state = np.zeros((self.n_rounds, 2 * self.n_letters), dtype=np.int64)
-
-        return self.state
-
-    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.states:
-                text = ''.join(map(
-                    self._highlighter, 
-                    to_english(row[:self.n_letters]).upper(), 
-                    row[self.n_letters:]
-                ))
-
-                print(text)
-        else:
-            super(WordleEnv, self).render(mode=mode)
-                
-    def step(self, action):
-        """Run one step of the Wordle game. Every game must be previously
-        initialized by a call to the `reset` method.
-
-        Args:
-            action: Word guessed by the agent.
-        Returns:
-            state (object): Wordle game state after the guess.
-            reward (float): Reward associated with the guess (-1 for incorrect,
-              0 for correct)
-            done (bool): Whether the game has ended (by a correct guess or
-              after six guesses).
-            info (dict): Auxiliary diagnostic information (empty).
-        """
-        assert self.action_space.contains(action), 'Invalid word!'
-
-        # transform the action, solution indices to their words
-        action = self.action_space[action]  
-        solution = self.solution_space[self.solution]
-
-        # populate the word chars into the row (character channel)
-        self.state[self.round][:self.n_letters] = action
-
-        # populate the flag characters into the row (flag channel)
-        counter = Counter()
-        for i, char in enumerate(action):
-            flag_i = i + self.n_letters  # starts at 5
-            counter[char] += 1
-
-            if char == solution[i]:  # character is in correct position
-                self.state[self.round, flag_i] = self.right_pos
-            elif counter[char] <= (char == solution).sum():  
-                # current character has been seen within correct number of
-                # occurrences 
-                self.state[self.round, flag_i] = self.wrong_pos
-            else:
-                # wrong character, or "correct" character too many times
-                self.state[self.round, flag_i] = self.wrong_char
-
-        self.round += 1
-
-        correct = (action == solution).all()
-        game_over = (self.round == self.n_rounds)
-
-        done = correct or game_over
-
-        # Total reward equals -(number of incorrect guesses)
-        # reward = 0. if correct else -1.
-        
-        # correct +10
-        # guesses new letter +1
-        # guesses correct letter +1
-        # spent another guess -1
-
-        reward = 0
-        reward += np.sum(self.state[:, 5:] == 1) * 1
-        reward += np.sum(self.state[:, 5:] == 2) * 0.5
-        reward += np.sum(self.state[:, 5:] == 3) * -1
-        reward += 10 if correct else -10 if done else 0
-
-        info = {'correct': correct}
-
-        return self.state, reward, done, info

wordle_gym/__init__.py

@@ -0,0 +1,9 @@
+from gym.envs.registration import register
+
+register(
+    id="wordle-v0", entry_point="wordle_gym.envs.wordle_env:WordleEnv",
+)
+
+register(
+    id="wordle-alpha-v0", entry_point="wordle_gym.envs.wordle_alpha_env:WordleEnv",
+)

wordle_gym/envs/strategies/base.py

@@ -0,0 +1,15 @@
+from enum import Enum
+
+from typing import List
+
+class StrategyType(Enum):
+    RANDOM = 1
+    ELIMINATION = 2
+    PROBABILITY = 3
+
+class Strategy:
+    def __init__(self, type: StrategyType):
+        self.type = type
+    
+    def get_best_word(self, guesses: List[List[str]], state: List[List[int]]):
+        raise NotImplementedError("Strategy.get_best_word() not implemented")

wordle_gym/envs/strategies/elimination.py

@@ -0,0 +1,2 @@
+def get_best_word(state):
+    

wordle_gym/envs/strategies/probabilistic.py

@@ -0,0 +1,20 @@
+from random import sample
+from typing import List
+
+from base import Strategy
+from base import StrategyType
+
+from utils import freq
+
+class Random(Strategy):
+    def __init__(self):
+        self.words = freq.get_5_letter_word_freqs()
+        super().__init__(StrategyType.RANDOM)
+
+    def get_best_word(self, state: List[List[int]]):
+        
+
+
+if __name__ == "__main__":
+    r = Random()
+    print(r.get_best_word([]))

wordle_gym/envs/strategies/rand.py

@@ -0,0 +1,29 @@
+from random import sample
+from typing import List
+
+from base import Strategy
+from base import StrategyType
+
+from utils import freq
+
+class Random(Strategy):
+    def __init__(self):
+        self.words = freq.get_5_letter_word_freqs()
+        super().__init__(StrategyType.RANDOM)
+
+    def get_best_word(self, guesses: List[List[str]], state: List[List[int]]):
+        correct_letters = []
+        regex = ""
+        for g, s in zip(guesses, state):
+            for c, s in zip(g, s):
+                if s == 2:
+                    correct_letters.append(c)
+                    regex += c
+                
+
+
+
+
+if __name__ == "__main__":
+    r = Random()
+    print(r.get_best_word([]))

wordle_gym/envs/strategies/utils/freq.py

@@ -0,0 +1,27 @@
+from os import path
+
+def get_5_letter_word_freqs():
+    """
+    Returns a list of words with 5 letters.
+    """
+    FILEPATH = path.join(path.dirname(path.abspath(__file__)), "data/norvig.txt")
+    lines = read_file(FILEPATH)
+    return {k:v for k, v in get_freq(lines).items() if len(k) == 5}
+
+
+def read_file(filename):
+    """
+    Reads a file and returns a list of words and frequencies
+    """
+    with open(filename, 'r') as f:
+        return f.readlines()
+
+
+def get_freq(lines):
+    """
+    Returns a dictionary of words and their frequencies
+    """
+    freqs = {}
+    for word, freq in map(lambda x: x.split("\t"), lines):
+        freqs[word] = int(freq)
+    return freqs

wordle_gym/envs/wordle_env.py

@@ -0,0 +1,131 @@
+import os
+
+
+import gym
+from gym import error, spaces, utils
+from gym.utils import seeding
+
+from enum import Enum
+from collections import Counter
+import numpy as np
+
+WORD_LENGTH = 5
+TOTAL_GUESSES = 6
+SOLUTION_PATH = "../words/solution.csv"
+VALID_WORDS_PATH = "../words/guess.csv"
+
+class LetterState(Enum):
+    ABSENT = 0
+    PRESENT = 1
+    CORRECT_POSITION = 2
+
+
+class WordleEnv(gym.Env):
+    metadata = {"render.modes": ["human"]}
+
+    def _current_path(self):
+        return os.path.dirname(os.path.abspath(__file__))
+
+    def _read_solutions(self):
+        return open(os.path.join(self._current_path(), SOLUTION_PATH)).read().splitlines()
+    
+    def _get_valid_words(self):
+        words = []
+        for word in open(os.path.join(self._current_path(), VALID_WORDS_PATH)).read().splitlines():
+            words.append((word, Counter(word)))
+        return words
+
+    def get_valid(self):
+        return self._valid_words
+
+    def __init__(self):
+        self._solutions = self._read_solutions()
+        self._valid_words = self._get_valid_words()
+        self.action_space = spaces.Discrete(len(self._valid_words))
+        self.observation_space = spaces.MultiDiscrete([3] * TOTAL_GUESSES * WORD_LENGTH)
+        np.random.seed(0)
+        self.reset()
+    
+    def _check_guess(self, guess, guess_counter):
+        c = guess_counter & self.solution_ct
+        result = []
+        correct = True
+        reward = 0
+        for i, char in enumerate(guess):
+            if c.get(char, 0) > 0:
+                if self.solution[i] == char:
+                    result.append(2)
+                    reward += 2
+                else:
+                    result.append(1)
+                    correct = False
+                    reward += 1
+                c[char] -= 1
+            else:
+                result.append(0)
+                correct = False
+        return result, correct, reward
+
+    def step(self, action):
+        """
+        action: index of word in valid_words
+
+        returns:
+            observation: (TOTAL_GUESSES, WORD_LENGTH)
+            reward: 0 if incorrect, 1 if correct, -1 if game over w/o final answer being obtained
+            done: True if game over, w/ or w/o correct answer
+            additional_info: empty
+        """
+        guess, guess_counter = self._valid_words[action]
+        if guess in self.guesses:
+            return self.obs, -1, False, {}
+        self.guesses.append(guess)
+        result, correct, reward = self._check_guess(guess, guess_counter)
+        done = False
+
+        for i in range(self.guess_no*WORD_LENGTH, self.guess_no*WORD_LENGTH + WORD_LENGTH):
+            self.obs[i] = result[i - self.guess_no*WORD_LENGTH]
+        
+        self.guess_no += 1
+        if correct:
+            done = True
+            reward = 1200
+        if self.guess_no == TOTAL_GUESSES:
+            done = True
+            if not correct:
+                reward = -15
+        return self.obs, reward, done, {}
+
+    def reset(self):
+        self.solution = self._solutions[np.random.randint(len(self._solutions))]
+        self.solution_ct = Counter(self.solution)
+        self.guess_no = 0
+        self.guesses = []
+        self.obs = np.zeros((TOTAL_GUESSES * WORD_LENGTH, ))
+        return self.obs
+
+    def render(self, mode="human"):
+        m = {
+            0: "⬜",
+            1: "🟨",
+            2: "🟩"
+        }
+        print("Solution:", self.solution)
+        for g, o in zip(self.guesses, np.reshape(self.obs, (TOTAL_GUESSES, WORD_LENGTH))):
+            o_n = "".join(map(lambda x: m[x], o))
+            print(g, o_n)
+
+    def close(self):
+        pass
+
+if __name__ == "__main__":
+    env = WordleEnv()
+    print(env.action_space)
+    print(env.observation_space)
+    print(env.solution)
+    print(env.step(0))
+    print(env.step(0))
+    print(env.step(0))
+    print(env.step(0))
+    print(env.step(0))
+    print(env.step(0))