still doesnt train

dqn_wordle.py

@@ -1,47 +1,40 @@
-# %%
-from stable_baselines3 import DQN
-import numpy as np
-import wordle.state
 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
 
-# %%
-env = gym.make("WordleEnvFull-v0")
+def train (model, env, total_timesteps = 100000): 
+    model.learn(total_timesteps=total_timesteps, progress_bar=True)
+    model.save("dqn_wordle")
 
-print(env)
+def test(model, env, test_num=1000):
 
-# %%
-total_timesteps = 100000
-model = DQN("MlpPolicy", env, verbose=0)
-model.learn(total_timesteps=total_timesteps, progress_bar=True)
+    total_correct = 0
 
-# %%
-def test(model):
+    for i in tqdm(range(test_num)):
 
-    end_rewards = []
-
-    for i in range(1000):
-        
-        state = env.reset()
+        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)
 
-            action, _states = model.predict(state, deterministic=True)
+            print(action, obs, rewards)
 
-            state, reward, done, info = env.step(action)
+    return total_correct / test_num
 
-        end_rewards.append(reward == 0)
-        
-    return np.sum(end_rewards) / len(end_rewards)
-
-# %%
-model.save("dqn_wordle")
-
-# %%
-model = DQN.load("dqn_wordle")
-
-# %%
-print(test(model))
+if __name__ == "__main__":
     
+    env = gym.make("wordle-v0")
+    model = DQN("MlpPolicy", env, verbose=0)
+    print(env)
+    print(model)
 
+    train(model, env, total_timesteps=10000)
+    print(test(model, env, test_num=1))

wordle/__init__.py

@@ -1,83 +0,0 @@
-from gym.envs.registration import (
-    registry,
-    register,
-    make,
-    spec,
-    load_env_plugins as _load_env_plugins,
-)
-
-
-# Classic
-# ----------------------------------------
-
-register(
-    id="WordleEnv10-v0",
-    entry_point="wordle.wordle:WordleEnv10",
-    max_episode_steps=200,
-)
-
-register(
-    id="WordleEnv100-v0",
-    entry_point="wordle.wordle:WordleEnv100",
-    max_episode_steps=500,
-)
-
-register(
-    id="WordleEnv100OneAction-v0",
-    entry_point="wordle.wordle:WordleEnv100OneAction",
-    max_episode_steps=500,
-)
-
-register(
-    id="WordleEnv100TwoAction-v0",
-    entry_point="wordle.wordle:WordleEnv100TwoAction",
-    max_episode_steps=500,
-)
-
-register(
-    id="WordleEnv100FullAction-v0",
-    entry_point="wordle.wordle:WordleEnv100FullAction",
-    max_episode_steps=500,
-)
-
-register(
-    id="WordleEnv100WithMask-v0",
-    entry_point="wordle.wordle:WordleEnv100WithMask",
-    max_episode_steps=500,
-)
-
-register(
-    id="WordleEnv1000-v0",
-    entry_point="wordle.wordle:WordleEnv1000",
-    max_episode_steps=500,
-)
-
-register(
-    id="WordleEnv1000WithMask-v0",
-    entry_point="wordle.wordle:WordleEnv1000WithMask",
-    max_episode_steps=500,
-)
-
-register(
-    id="WordleEnv1000FullAction-v0",
-    entry_point="wordle.wordle:WordleEnv1000FullAction",
-    max_episode_steps=500,
-)
-
-register(
-    id="WordleEnvFull-v0",
-    entry_point="wordle.wordle:WordleEnvFull",
-    max_episode_steps=500,
-)
-
-register(
-    id="WordleEnvReal-v0",
-    entry_point="wordle.wordle:WordleEnvReal",
-    max_episode_steps=500,
-)
-
-register(
-    id="WordleEnvRealWithMask-v0",
-    entry_point="wordle.wordle:WordleEnvRealWithMask",
-    max_episode_steps=500,
-)

wordle/const.py

@@ -1,3 +0,0 @@
-WORDLE_CHARS = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
-WORDLE_N = 5
-REWARD = 10

wordle/state.py

@@ -1,162 +0,0 @@
-"""
-Keep the state in a 1D int array
-
-index[0] = remaining steps
-Rest of data is laid out as binary array
-
-[1..27] = whether char has been guessed or not
-
-[[status, status, status, status, status]
- for _ in "ABCD..."]
-where status has codes
- [1, 0, 0] - char is definitely not in this spot
- [0, 1, 0] - char is maybe in this spot
- [0, 0, 1] - char is definitely in this spot
-"""
-import collections
-from typing import List
-import numpy as np
-
-from wordle.const import WORDLE_CHARS, WORDLE_N
-
-
-WordleState = np.ndarray
-
-
-def get_nvec(max_turns: int):
-    return [max_turns] + [2] * len(WORDLE_CHARS) + [2] * 3 * WORDLE_N * len(WORDLE_CHARS)
-
-
-def new(max_turns: int) -> WordleState:
-    return np.array(
-        [max_turns] + [0] * len(WORDLE_CHARS) + [0, 1, 0] * WORDLE_N * len(WORDLE_CHARS),
-        dtype=np.int32)
-
-
-def remaining_steps(state: WordleState) -> int:
-    return state[0]
-
-
-NO = 0
-SOMEWHERE = 1
-YES = 2
-
-
-def update_from_mask(state: WordleState, word: str, mask: List[int]) -> WordleState:
-    """
-    return a copy of state that has been updated to new state
-
-    From a mask we need slighty different logic since we don't know the
-    goal word.
-
-    :param state:
-    :param word:
-    :param goal_word:
-    :return:
-    """
-    state = state.copy()
-
-    prior_yes = []
-    prior_maybe = []
-    # We need two passes because first pass sets definitely yesses
-    # second pass sets the no's for those who aren't already yes
-    state[0] -= 1
-    for i, c in enumerate(word):
-        cint = ord(c) - ord(WORDLE_CHARS[0])
-        offset = 1 + len(WORDLE_CHARS) + cint * WORDLE_N * 3
-        state[1 + cint] = 1
-        if mask[i] == YES:
-            prior_yes.append(c)
-            # char at position i = yes, all other chars at position i == no
-            state[offset + 3 * i:offset + 3 * i + 3] = [0, 0, 1]
-            for ocint in range(len(WORDLE_CHARS)):
-                if ocint != cint:
-                    oc_offset = 1 + len(WORDLE_CHARS) + ocint * WORDLE_N * 3
-                    state[oc_offset + 3 * i:oc_offset + 3 * i + 3] = [1, 0, 0]
-
-    for i, c in enumerate(word):
-        cint = ord(c) - ord(WORDLE_CHARS[0])
-        offset = 1 + len(WORDLE_CHARS) + cint * WORDLE_N * 3
-        if mask[i] == SOMEWHERE:
-            prior_maybe.append(c)
-            # Char at position i = no, other chars stay as they are
-            state[offset + 3 * i:offset + 3 * i + 3] = [1, 0, 0]
-        elif mask[i] == NO:
-            # Need to check this first in case there's prior maybe + yes
-            if c in prior_maybe:
-                # Then the maybe could be anywhere except here
-                state[offset+3*i:offset+3*i+3] = [1, 0, 0]
-            elif c in prior_yes:
-                # No maybe, definitely a yes, so it's zero everywhere except the yesses
-                for j in range(WORDLE_N):
-                    # Only flip no if previously was maybe
-                    if state[offset + 3 * j:offset + 3 * j + 3][1] == 1:
-                        state[offset + 3 * j:offset + 3 * j + 3] = [1, 0, 0]
-            else:
-                # Just straight up no
-                state[offset:offset+3*WORDLE_N] = [1, 0, 0]*WORDLE_N
-
-    return state
-
-
-def get_mask(word: str, goal_word: str) -> List[int]:
-    # Definite yesses first
-    mask = [0, 0, 0, 0, 0]
-    counts = collections.Counter(goal_word)
-    for i, c in enumerate(word):
-        if goal_word[i] == c:
-            mask[i] = 2
-            counts[c] -= 1
-
-    for i, c in enumerate(word):
-        if mask[i] == 2:
-            continue
-        elif c in counts:
-            if counts[c] > 0:
-                mask[i] = 1
-                counts[c] -= 1
-            else:
-                for j in range(i+1, len(mask)):
-                    if mask[j] == 2:
-                        continue
-                    mask[j] = 0
-
-    return mask
-
-def update_mask(state: WordleState, word: str, goal_word: str) -> WordleState:
-    """
-    return a copy of state that has been updated to new state
-
-    :param state:
-    :param word:
-    :param goal_word:
-    :return:
-    """
-    mask = get_mask(word, goal_word)
-    return update_from_mask(state, word, mask)
-
-
-def update(state: WordleState, word: str, goal_word: str) -> WordleState:
-    state = state.copy()
-
-    state[0] -= 1
-    for i, c in enumerate(word):
-        cint = ord(c) - ord(WORDLE_CHARS[0])
-        offset = 1 + len(WORDLE_CHARS) + cint * WORDLE_N * 3
-        state[1 + cint] = 1
-        if goal_word[i] == c:
-            # char at position i = yes, all other chars at position i == no
-            state[offset + 3 * i:offset + 3 * i + 3] = [0, 0, 1]
-            for ocint in range(len(WORDLE_CHARS)):
-                if ocint != cint:
-                    oc_offset = 1 + len(WORDLE_CHARS) + ocint * WORDLE_N * 3
-                    state[oc_offset + 3 * i:oc_offset + 3 * i + 3] = [1, 0, 0]
-        elif c in goal_word:
-            # Char at position i = no, other chars stay as they are
-            state[offset + 3 * i:offset + 3 * i + 3] = [1, 0, 0]
-        else:
-            # Char at all positions = no
-            state[offset:offset + 3 * WORDLE_N] = [1, 0, 0] * WORDLE_N
-
-    return state
-

wordle/wordle.py

@@ -1,173 +0,0 @@
-import os
-from typing import Optional, List
-
-import gym
-from gym import spaces
-import numpy as np
-
-import wordle.state
-from wordle.const import WORDLE_N, REWARD
-
-CUR_PATH = os.environ.get('PYTHONPATH', '.')
-import os
-dirname = os.path.dirname(__file__)
-VALID_WORDS_PATH = f'{dirname}/wordle_words.txt'
-
-
-def _load_words(limit: Optional[int]=None) -> List[str]:
-    with open(VALID_WORDS_PATH, 'r') as f:
-        lines = [x.strip().upper() for x in f.readlines()]
-        if not limit:
-            return lines
-        else:
-            return lines[:limit]
-
-
-class WordleEnvBase(gym.Env):
-    """
-    Actions:
-        Can play any 5 letter word in vocabulary
-        * 13k for full vocab
-    State space is defined as:
-        * 6 possibilities for turns (WORDLE_TURNS)
-        * Each VALID_CHAR has a state of 0/1 for whether it's been guessed before
-        * For each in VALID_CHARS [A-Z] can be in one of 3^WORDLE_N states: (No, Maybe, Yes)
-        for full game, this is (3^5)^26
-        Each state has 1 + 5*26 possibilities
-    Reward:
-        Reward is 10 for guessing the right word, -10 for not guessing the right word after 6 guesses.
-    Starting State:
-        Random goal word
-        Initial state with turn 0, all chars Unvisited + Maybe
-    """
-    def __init__(self, words: List[str],
-                 max_turns: int,
-                 allowable_words: Optional[int] = None,
-                 frequencies: Optional[List[float]]=None,
-                 mask_based_state_updates: bool=False):
-        assert all(len(w) == WORDLE_N for w in words), f'Not all words of length {WORDLE_N}, {words}'
-        self.words = words
-        self.max_turns = max_turns
-        self.allowable_words = allowable_words
-        self.mask_based_state_updates = mask_based_state_updates
-        if not self.allowable_words:
-            self.allowable_words = len(self.words)
-
-        self.frequencies = None
-        if frequencies:
-            assert len(words) == len(frequencies), f'{len(words), len(frequencies)}'
-            self.frequencies = np.array(frequencies, dtype=np.float32) / sum(frequencies)
-
-        self.action_space = spaces.Discrete(len(self.words))
-        self.observation_space = spaces.MultiDiscrete(wordle.state.get_nvec(self.max_turns))
-
-        self.done = True
-        self.goal_word: int = -1
-
-        self.state: wordle.state.WordleState = None
-        self.state_updater = wordle.state.update
-        if self.mask_based_state_updates:
-            self.state_updater = wordle.state.update_mask
-
-    def step(self, action: int):
-        if self.done:
-            raise ValueError(
-                "You are calling 'step()' even though this "
-                "environment has already returned done = True. You "
-                "should always call 'reset()' once you receive 'done = "
-                "True' -- any further steps are undefined behavior."
-            )
-        self.state = self.state_updater(state=self.state,
-                                        word=self.words[action],
-                                        goal_word=self.words[self.goal_word])
-
-        reward = 0
-        if action == self.goal_word:
-            self.done = True
-            #reward = REWARD
-            if wordle.state.remaining_steps(self.state) == self.max_turns-1:
-                reward = 0#-10*REWARD  # No reward for guessing off the bat
-            else:
-                #reward = REWARD*(self.state.remaining_steps() + 1) / self.max_turns
-                reward = REWARD
-        elif wordle.state.remaining_steps(self.state) == 0:
-            self.done = True
-            reward = -REWARD
-
-        return self.state.copy(), reward, self.done, False, {"goal_id": self.goal_word}
-
-    def reset(self, options = None, seed: Optional[int] = None):
-        self.state = wordle.state.new(self.max_turns)
-        self.done = False
-        self.goal_word = int(np.random.random()*self.allowable_words)
-
-        return self.state.copy(), {"goal_id": self.goal_word}
-
-    def set_goal_word(self, goal_word: str):
-        self.goal_word = self.words.index(goal_word)
-
-    def set_goal_id(self, goal_id: int):
-        self.goal_word = goal_id
-
-
-class WordleEnv10(WordleEnvBase):
-    def __init__(self):
-        super().__init__(words=_load_words(10), max_turns=6)
-
-
-class WordleEnv100(WordleEnvBase):
-    def __init__(self):
-        super().__init__(words=_load_words(100), max_turns=6)
-
-
-class WordleEnv100OneAction(WordleEnvBase):
-    def __init__(self):
-        super().__init__(words=_load_words(100), allowable_words=1, max_turns=6)
-
-
-class WordleEnv100WithMask(WordleEnvBase):
-    def __init__(self):
-        super().__init__(words=_load_words(100), max_turns=6,
-                         mask_based_state_updates=True)
-
-
-class WordleEnv100TwoAction(WordleEnvBase):
-    def __init__(self):
-        super().__init__(words=_load_words(100), allowable_words=2, max_turns=6)
-
-
-class WordleEnv100FullAction(WordleEnvBase):
-    def __init__(self):
-        super().__init__(words=_load_words(), allowable_words=100, max_turns=6)
-
-
-class WordleEnv1000(WordleEnvBase):
-    def __init__(self):
-        super().__init__(words=_load_words(1000), max_turns=6)
-
-
-class WordleEnv1000WithMask(WordleEnvBase):
-    def __init__(self):
-        super().__init__(words=_load_words(1000), max_turns=6,
-                         mask_based_state_updates=True)
-
-
-class WordleEnv1000FullAction(WordleEnvBase):
-    def __init__(self):
-        super().__init__(words=_load_words(), allowable_words=1000, max_turns=6)
-
-
-class WordleEnvFull(WordleEnvBase):
-    def __init__(self):
-        super().__init__(words=_load_words(), max_turns=6)
-
-
-class WordleEnvReal(WordleEnvBase):
-    def __init__(self):
-        super().__init__(words=_load_words(), allowable_words=2315, max_turns=6)
-
-
-class WordleEnvRealWithMask(WordleEnvBase):
-    def __init__(self):
-        super().__init__(words=_load_words(), allowable_words=2315, max_turns=6,
-                         mask_based_state_updates=True)

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))