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delete gym-wordle, fix some issues in letter_guess gym, add wandb integration
This commit is contained in:
Arthur Lu
parent
fc197acb6e
commit
f40301cac9
2
.gitignore
vendored
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.gitignore
vendored
@ -2,3 +2,5 @@
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**/*.zip
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**/__pycache__
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/env
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**/runs/*
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**/wandb/*
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from gym.envs.registration import register
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from .wordle import WordleEnv
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register(
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id='Wordle-v0',
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entry_point='gym_wordle.wordle:WordleEnv'
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)
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import numpy as np
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import numpy.typing as npt
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from pathlib import Path
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_chars = ' abcdefghijklmnopqrstuvwxyz'
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_char_d = {c: i for i, c in enumerate(_chars)}
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def to_english(array: npt.NDArray[np.int64]) -> str:
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"""Converts a numpy integer array into a corresponding English string.
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Args:
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array: Word in array (int) form. It is assumed that each integer in the
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array is between 0,...,26 (inclusive).
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Returns:
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A (lowercase) string representation of the word.
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"""
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return ''.join(_chars[i] for i in array)
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def to_array(word: str) -> npt.NDArray[np.int64]:
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"""Converts a string of characters into a corresponding numpy array.
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Args:
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word: Word in string form. It is assumed that each character in the
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string is either an empty space ' ' or lowercase alphabetical
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character.
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Returns:
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An array representation of the word.
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"""
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return np.array([_char_d[c] for c in word])
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def get_words(category: str, build: bool = False) -> npt.NDArray[np.int64]:
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"""Loads a list of words in array form.
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If specified, this will recompute the list from the human-readable list of
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words, and save the results in array form.
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Args:
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category: Either 'guess' or 'solution', which corresponds to the list
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of acceptable guess words and the list of acceptable solution words.
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build: If True, recomputes and saves the array-version of the computed
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list for future access.
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Returns:
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An array representation of the list of words specified by the category.
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This array has two dimensions, and the number of columns is fixed at
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five.
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"""
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assert category in {'guess', 'solution'}
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arr_path = Path(__file__).parent / f'dictionary/{category}_list.npy'
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if build:
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list_path = Path(__file__).parent / f'dictionary/{category}_list.csv'
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with open(list_path, 'r') as f:
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words = np.array([to_array(line.strip()) for line in f])
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np.save(arr_path, words)
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return np.load(arr_path)
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def play():
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"""Play Wordle yourself!"""
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import gym
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import gym_wordle
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env = gym.make('Wordle-v0') # load the environment
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env.reset()
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solution = to_english(env.unwrapped.solution_space[env.solution]).upper() # no peeking!
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done = False
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while not done:
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action = -1
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# in general, the environment won't be forgiving if you input an
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# invalid word, but for this function I want to let you screw up user
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# input without consequence, so just loops until valid input is taken
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while not env.action_space.contains(action):
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guess = input('Guess: ')
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action = env.unwrapped.action_space.index_of(to_array(guess))
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state, reward, done, info = env.step(action)
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env.render()
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print(f"The word was {solution}")
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@ -1,353 +0,0 @@
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import gymnasium as gym
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import numpy as np
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import numpy.typing as npt
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from sty import fg, bg, ef, rs
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from collections import Counter
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from gym_wordle.utils import to_english, to_array, get_words
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from typing import Optional
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from collections import defaultdict
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class WordList(gym.spaces.Discrete):
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"""Super class for defining a space of valid words according to a specified
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list.
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The space is a subclass of gym.spaces.Discrete, where each element
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corresponds to an index of a valid word in the word list. The obfuscation
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is necessary for more direct implementation of RL algorithms, which expect
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spaces of less sophisticated form.
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In addition to the default methods of the Discrete space, it implements
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a __getitem__ method for easy index lookup, and an index_of method to
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convert potential words into their corresponding index (if they exist).
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"""
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def __init__(self, words: npt.NDArray[np.int64], **kwargs):
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"""
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Args:
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words: Collection of words in array form with shape (_, 5), where
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each word is a row of the array. Each array element is an integer
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between 0,...,26 (inclusive).
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kwargs: See documentation for gym.spaces.MultiDiscrete
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"""
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super().__init__(words.shape[0], **kwargs)
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self.words = words
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def __getitem__(self, index: int) -> npt.NDArray[np.int64]:
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"""Obtains the (int-encoded) word associated with the given index.
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Args:
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index: Index for the list of words.
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Returns:
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Associated word at the position specified by index.
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"""
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return self.words[index]
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def index_of(self, word: npt.NDArray[np.int64]) -> int:
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"""Given a word, determine its index in the list (if it exists),
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otherwise returning -1 if no index exists.
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Args:
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word: Word to find in the word list.
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Returns:
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The index of the given word if it exists, otherwise -1.
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"""
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try:
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index, = np.nonzero((word == self.words).all(axis=1))
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return index[0]
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except:
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return -1
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class SolutionList(WordList):
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"""Space for *solution* words to the Wordle environment.
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In the game Wordle, there are two different collections of words:
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* "guesses", which the game accepts as valid words to use to guess the
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answer.
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* "solutions", which the game uses to choose solutions from.
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Of course, the set of solutions is a strict subset of the set of guesses.
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This class represents the set of solution words.
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"""
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def __init__(self, **kwargs):
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"""
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Args:
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kwargs: See documentation for gym.spaces.MultiDiscrete
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"""
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words = get_words('solution')
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super().__init__(words, **kwargs)
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class WordleObsSpace(gym.spaces.Box):
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"""Implementation of the state (observation) space in terms of gym
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primitives, in this case, gym.spaces.Box.
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The Wordle observation space can be thought of as a 6x5 array with two
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channels:
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- the character channel, indicating which characters are placed on the
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board (unfilled rows are marked with the empty character, 0)
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- the flag channel, indicating the in-game information associated with
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each character's placement (green highlight, yellow highlight, etc.)
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where there are 6 rows, one for each turn in the game, and 5 columns, since
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the solution will always be a word of length 5.
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For simplicity, and compatibility with stable_baselines algorithms,
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this multichannel is modeled as a 6x10 array, where the two channels are
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horizontally appended (along columns). Thus each row in the observation
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should be interpreted as c0 c1 c2 c3 c4 f0 f1 f2 f3 f4 when the word is
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c0...c4 and its associated flags are f0...f4.
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"""
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def __init__(self, **kwargs):
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self.n_rows = 6
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self.n_cols = 5
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self.max_char = 26
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self.max_flag = 4
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low = np.zeros((self.n_rows, 2*self.n_cols))
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high = np.c_[np.full((self.n_rows, self.n_cols), self.max_char),
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np.full((self.n_rows, self.n_cols), self.max_flag)]
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super().__init__(low, high, dtype=np.int64, **kwargs)
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class GuessList(WordList):
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"""Space for *guess* words to the Wordle environment.
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This class represents the set of guess words.
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"""
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def __init__(self, **kwargs):
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"""
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Args:
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kwargs: See documentation for gym.spaces.MultiDiscrete
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"""
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words = get_words('guess')
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super().__init__(words, **kwargs)
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class WordleEnv(gym.Env):
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metadata = {'render.modes': ['human']}
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# Character flag codes
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no_char = 0
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right_pos = 1
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wrong_pos = 2
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wrong_char = 3
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def __init__(self):
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super().__init__()
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self.action_space = GuessList()
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self.solution_space = SolutionList()
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# Example setup based on the flattened state size you're now using
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num_position_availability = 26 * 5 # 26 letters for each of the 5 positions
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num_global_availability = 26 # Global letter availability
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num_letter_found_flags = 5 # One flag for each position
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total_size = num_position_availability + num_global_availability + num_letter_found_flags
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# Define the observation space to match the flattened state format
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self.observation_space = gym.spaces.Box(low=0, high=2, shape=(total_size,), dtype=np.float32)
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self._highlights = {
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self.right_pos: (bg.green, bg.rs),
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self.wrong_pos: (bg.yellow, bg.rs),
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self.wrong_char: ('', ''),
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self.no_char: ('', ''),
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}
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self.n_rounds = 6
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self.n_letters = 5
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self.info = {
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'correct': False,
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'guesses': set(),
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'known_positions': np.full(5, -1), # -1 for unknown, else letter index
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'known_letters': set(), # Letters known to be in the word
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'not_in_word': set(), # Letters known not to be in the word
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'tried_positions': defaultdict(set) # Positions tried for each letter
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}
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self.reset()
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def _highlighter(self, char: str, flag: int) -> str:
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"""Terminal renderer functionality. Properly highlights a character
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based on the flag associated with it.
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Args:
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char: Character in question.
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flag: Associated flag, one of:
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- 0: no character (render no background)
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- 1: right position (render green background)
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- 2: wrong position (render yellow background)
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- 3: wrong character (render no background)
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Returns:
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Correct ASCII sequence producing the desired character in the
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correct background.
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"""
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front, back = self._highlights[flag]
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return front + char + back
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def reset(self, seed=None, options=None):
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"""Reset the environment to an initial state and returns an initial
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observation.
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Note: The observation space instance should be a Box space.
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Returns:
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state (object): The initial observation of the space.
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"""
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self.round = 0
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self.solution = self.solution_space.sample()
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self.soln_hash = set(self.solution_space[self.solution])
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self.state = {
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'position_availability': [np.ones(26) for _ in range(5)], # Each position can initially have any letter
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'global_availability': np.ones(26), # Initially, all letters are available
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'letter_found': np.zeros(5) # Initially, no correct letters are found
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}
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self.info = {
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'correct': False,
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'guesses': set(),
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'known_positions': np.full(5, -1),
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'known_letters': set(),
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'not_in_word': set(),
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'tried_positions': defaultdict(set)
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}
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self.simulate_first_guess()
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return self.get_observation(), self.info
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def simulate_first_guess(self):
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fixed_first_guess = "rates" # Example: Using 'rates' as the fixed first guess
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# Convert the fixed guess into the appropriate format (e.g., indices of letters)
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fixed_guess_indices = to_array(fixed_first_guess)
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solution_indices = self.solution_space[self.solution]
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for pos in range(5): # Iterate over each position in the word
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letter_idx = fixed_guess_indices[pos]
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if letter_idx == solution_indices[pos]: # Correct letter in the correct position
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self.state['position_availability'][pos] = np.zeros(26)
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self.state['position_availability'][pos][letter_idx] = 1
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self.state['letter_found'][pos] = 1
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elif letter_idx in solution_indices: # Correct letter in the wrong position
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self.state['position_availability'][pos][letter_idx] = 0
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# Mark this letter as still available in other positions
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for other_pos in range(5):
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if self.state['letter_found'][other_pos] == 0: # If not already found
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self.state['position_availability'][other_pos][letter_idx] = 1
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else: # Letter not in the word
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self.state['global_availability'][letter_idx] = 0
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# Update all positions to reflect this letter is not in the word
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for other_pos in range(5):
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self.state['position_availability'][other_pos][letter_idx] = 0
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self.round = 1 # Increment round to reflect that first guess has been simulated
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def render(self, mode: str = 'human'):
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"""Renders the Wordle environment.
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Currently supported render modes:
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- human: renders the Wordle game to the terminal.
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Args:
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mode: the mode to render with.
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"""
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if mode == 'human':
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for row in self.state:
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text = ''.join(map(
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self._highlighter,
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to_english(row[:self.n_letters]).upper(),
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row[self.n_letters:]
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))
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print(text)
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else:
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super().render(mode=mode)
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def step(self, action):
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assert self.action_space.contains(action), 'Invalid word!'
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guessed_word = self.action_space[action]
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solution_word = self.solution_space[self.solution]
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reward = 0
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correct_guess = np.array_equal(guessed_word, solution_word)
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# Initialize flags for current guess based on the new state structure
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current_flags = np.zeros((self.n_letters, 26)) # Replaced with a more detailed flag system
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# Track newly discovered information
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new_info = False
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for i in range(self.n_letters):
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guessed_letter = guessed_word[i] - 1
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if guessed_letter in solution_word:
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if guessed_letter in self.info['not_in_word']:
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reward -= 2 # Penalize for reusing a letter found to not be in the word
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if guessed_letter == solution_word[i]:
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# Handle correct letter in the correct position
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current_flags[i, :] = 0 # Set all other letters to not possible
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current_flags[i, guessed_letter] = 2 # Mark this letter as correct
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self.info['known_positions'][i] = 1 # Update known_positions
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reward += 10 # Reward for correct placement
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new_info = True
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else:
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# Correct letter, wrong position
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if self.info['known_positions'][i] == 0:
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# Only update if we haven't already found the correct letter for this position
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current_flags[:, guessed_letter] = 2 # Mark this letter as found in another position
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reward += 5
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new_info = True
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else:
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# Letter not in word
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if guessed_letter not in self.info['not_in_word']:
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self.info['not_in_word'].add(guessed_letter)
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reward -= 2 # Penalize for guessing a letter not in the word
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new_info = True
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for pos in range(self.n_letters):
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# Update all positions to reflect this letter is not correct
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current_flags[pos, guessed_letter] = 0
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# Update global letter availability based on the guess
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for letter in range(26):
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if letter not in guessed_word or letter in self.info['not_in_word']:
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self.state['global_availability'][letter] = 0
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# Check if the game is over
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done = self.round == self.n_rounds - 1 or correct_guess
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self.info['correct'] = correct_guess
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if correct_guess:
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reward += 100 # Major reward for winning
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elif done:
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reward -= 50 # Penalty for losing without using new information effectively
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elif not new_info:
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reward -= 10 # Penalty if no new information was used in this guess
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self.round += 1
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return self.get_observation(), reward, done, False, self.info
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def get_observation(self):
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# Flatten the position-specific letter availability
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position_availability_flat = np.concatenate(self.state['position_availability'])
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# Global availability is already a 1D array, but ensure consistency in data handling
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global_availability_flat = self.state['global_availability'].flatten()
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# Concatenate all parts of the state into a single flat array for the DQN input
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full_state_flat = np.concatenate(
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[position_availability_flat, global_availability_flat, self.state['letter_found']])
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return full_state_flat
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@ -15,18 +15,17 @@ class LetterGuessingEnv(gym.Env):
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metadata = {'render_modes': ['human']}
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def __init__(self, valid_words, seed):
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def __init__(self, valid_words, seed=None):
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self.action_space = spaces.Discrete(26)
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self.observation_space = spaces.Box(low=0, high=2, shape=(26*2 + 26*4,), dtype=np.int32)
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self.observation_space = spaces.Box(low=0, high=1, shape=(26*2 + 26*4,), dtype=np.int32)
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self.valid_words = valid_words # List of valid Wordle words
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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()
|
||||
|
||||
@ -47,14 +46,19 @@ 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
|
||||
if position == 4:
|
||||
self.letter_positions[:,:] = 1
|
||||
else:
|
||||
self.letter_positions[:, position] = 0
|
||||
self.letter_positions[letter_index, position] = 1
|
||||
|
||||
@ -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,))
|
||||
|
||||
Loading…
Reference in New Issue
Block a user