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https://github.com/ltcptgeneral/cse151b-final-project.git
synced 2025-09-08 16:27:21 +00:00
started new letter guess environment
This commit is contained in:
Ethan Shapiro
@@ -150,7 +150,14 @@ class WordleEnv(gym.Env):
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self.action_space = GuessList()
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self.solution_space = SolutionList()
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self.observation_space = WordleObsSpace()
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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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@@ -169,6 +176,7 @@ class WordleEnv(gym.Env):
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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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@@ -202,7 +210,11 @@ class WordleEnv(gym.Env):
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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 = np.zeros((self.n_rounds, 2 * self.n_letters), dtype=np.int64)
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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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@@ -215,41 +227,32 @@ class WordleEnv(gym.Env):
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self.simulate_first_guess()
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return self.state, self.info
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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"
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fixed_first_guess_array = to_array(fixed_first_guess)
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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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# Simulate the feedback for each letter in the fixed first guess
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feedback = np.zeros(self.n_letters, dtype=int) # Initialize feedback array
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for i, letter in enumerate(fixed_first_guess_array):
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if letter in self.solution_space[self.solution]:
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if letter == self.solution_space[self.solution][i]:
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feedback[i] = 1 # Correct position
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else:
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feedback[i] = 2 # Correct letter, wrong position
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else:
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feedback[i] = 3 # Letter not in word
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# Update the state to reflect the fixed first guess and its feedback
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self.state[0, :self.n_letters] = fixed_first_guess_array
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self.state[0, self.n_letters:] = feedback
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# Update self.info based on the feedback
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for i, flag in enumerate(feedback):
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if flag == self.right_pos:
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# Mark letter as correctly placed
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self.info['known_positions'][i] = fixed_first_guess_array[i]
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elif flag == self.wrong_pos:
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# Note the letter is in the word but in a different position
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self.info['known_letters'].add(fixed_first_guess_array[i])
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elif flag == self.wrong_char:
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# Note the letter is not in the word
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self.info['not_in_word'].add(fixed_first_guess_array[i])
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# Since we're simulating the first guess, increment the round counter
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self.round = 1
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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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@@ -280,49 +283,46 @@ class WordleEnv(gym.Env):
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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
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current_flags = np.full(self.n_letters, self.wrong_char)
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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]
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guessed_letter = guessed_word[i] - 1
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if guessed_letter in solution_word:
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# Penalize for reusing a letter found to not be in the word
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if guessed_letter in self.info['not_in_word']:
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reward -= 2
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reward -= 2 # Penalize for reusing a letter found to not be in the word
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# Handle correct letter in the correct position
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if guessed_letter == solution_word[i]:
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current_flags[i] = self.right_pos
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if self.info['known_positions'][i] != guessed_letter:
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reward += 10 # Large reward for new correct placement
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new_info = True
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self.info['known_positions'][i] = guessed_letter
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else:
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reward += 20 # Large reward for repeating correct placement
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else:
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current_flags[i] = self.wrong_pos
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if guessed_letter not in self.info['known_letters'] or i not in self.info['tried_positions'][guessed_letter]:
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reward += 10 # Reward for guessing a letter in a new position
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new_info = True
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else:
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reward -= 20 # Penalize for not leveraging known information
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self.info['known_letters'].add(guessed_letter)
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self.info['tried_positions'][guessed_letter].add(i)
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else:
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# New incorrect letter
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if guessed_letter not in self.info['not_in_word']:
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reward -= 2 # Penalize for guessing a letter not in the word
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self.info['not_in_word'].add(guessed_letter)
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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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reward -= 15 # Larger penalty for repeating an incorrect letter
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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 observation state with the current guess and flags
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self.state[self.round, :self.n_letters] = guessed_word
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self.state[self.round, self.n_letters:] = current_flags
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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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@@ -337,4 +337,17 @@ class WordleEnv(gym.Env):
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self.round += 1
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return self.state, reward, done, False, self.info
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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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