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rl/Lib/site-packages/gymnasium/envs/toy_text/__init__.py

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+from gymnasium.envs.toy_text.blackjack import BlackjackEnv
+from gymnasium.envs.toy_text.cliffwalking import CliffWalkingEnv
+from gymnasium.envs.toy_text.frozen_lake import FrozenLakeEnv
+from gymnasium.envs.toy_text.taxi import TaxiEnv

rl/Lib/site-packages/gymnasium/envs/toy_text/blackjack.py

@@ -0,0 +1,353 @@
+import os
+from typing import Optional
+
+import numpy as np
+
+import gymnasium as gym
+from gymnasium import spaces
+from gymnasium.error import DependencyNotInstalled
+
+
+def cmp(a, b):
+    return float(a > b) - float(a < b)
+
+
+# 1 = Ace, 2-10 = Number cards, Jack/Queen/King = 10
+deck = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10]
+
+
+def draw_card(np_random):
+    return int(np_random.choice(deck))
+
+
+def draw_hand(np_random):
+    return [draw_card(np_random), draw_card(np_random)]
+
+
+def usable_ace(hand):  # Does this hand have a usable ace?
+    return int(1 in hand and sum(hand) + 10 <= 21)
+
+
+def sum_hand(hand):  # Return current hand total
+    if usable_ace(hand):
+        return sum(hand) + 10
+    return sum(hand)
+
+
+def is_bust(hand):  # Is this hand a bust?
+    return sum_hand(hand) > 21
+
+
+def score(hand):  # What is the score of this hand (0 if bust)
+    return 0 if is_bust(hand) else sum_hand(hand)
+
+
+def is_natural(hand):  # Is this hand a natural blackjack?
+    return sorted(hand) == [1, 10]
+
+
+class BlackjackEnv(gym.Env):
+    """
+    Blackjack is a card game where the goal is to beat the dealer by obtaining cards
+    that sum to closer to 21 (without going over 21) than the dealers cards.
+
+    ## Description
+    The game starts with the dealer having one face up and one face down card,
+    while the player has two face up cards. All cards are drawn from an infinite deck
+    (i.e. with replacement).
+
+    The card values are:
+    - Face cards (Jack, Queen, King) have a point value of 10.
+    - Aces can either count as 11 (called a 'usable ace') or 1.
+    - Numerical cards (2-9) have a value equal to their number.
+
+    The player has the sum of cards held. The player can request
+    additional cards (hit) until they decide to stop (stick) or exceed 21 (bust,
+    immediate loss).
+
+    After the player sticks, the dealer reveals their facedown card, and draws cards
+    until their sum is 17 or greater. If the dealer goes bust, the player wins.
+
+    If neither the player nor the dealer busts, the outcome (win, lose, draw) is
+    decided by whose sum is closer to 21.
+
+    This environment corresponds to the version of the blackjack problem
+    described in Example 5.1 in Reinforcement Learning: An Introduction
+    by Sutton and Barto [<a href="#blackjack_ref">1</a>].
+
+    ## Action Space
+    The action shape is `(1,)` in the range `{0, 1}` indicating
+    whether to stick or hit.
+
+    - 0: Stick
+    - 1: Hit
+
+    ## Observation Space
+    The observation consists of a 3-tuple containing: the player's current sum,
+    the value of the dealer's one showing card (1-10 where 1 is ace),
+    and whether the player holds a usable ace (0 or 1).
+
+    The observation is returned as `(int(), int(), int())`.
+
+    ## Starting State
+    The starting state is initialised in the following range.
+
+    | Observation               | Min  | Max  |
+    |---------------------------|------|------|
+    | Player current sum        |  4   |  12  |
+    | Dealer showing card value |  2   |  11  |
+    | Usable Ace                |  0   |  1   |
+
+    ## Rewards
+    - win game: +1
+    - lose game: -1
+    - draw game: 0
+    - win game with natural blackjack:
+    +1.5 (if <a href="#nat">natural</a> is True)
+    +1 (if <a href="#nat">natural</a> is False)
+
+    ## Episode End
+    The episode ends if the following happens:
+
+    - Termination:
+    1. The player hits and the sum of hand exceeds 21.
+    2. The player sticks.
+
+    An ace will always be counted as usable (11) unless it busts the player.
+
+    ## Information
+
+    No additional information is returned.
+
+    ## Arguments
+
+    ```python
+    import gymnasium as gym
+    gym.make('Blackjack-v1', natural=False, sab=False)
+    ```
+
+    <a id="nat"></a>`natural=False`: Whether to give an additional reward for
+    starting with a natural blackjack, i.e. starting with an ace and ten (sum is 21).
+
+    <a id="sab"></a>`sab=False`: Whether to follow the exact rules outlined in the book by
+    Sutton and Barto. If `sab` is `True`, the keyword argument `natural` will be ignored.
+    If the player achieves a natural blackjack and the dealer does not, the player
+    will win (i.e. get a reward of +1). The reverse rule does not apply.
+    If both the player and the dealer get a natural, it will be a draw (i.e. reward 0).
+
+    ## References
+    <a id="blackjack_ref"></a>[1] R. Sutton and A. Barto, “Reinforcement Learning:
+    An Introduction” 2020. [Online]. Available: [http://www.incompleteideas.net/book/RLbook2020.pdf](http://www.incompleteideas.net/book/RLbook2020.pdf)
+
+    ## Version History
+    * v1: Fix the natural handling in Blackjack
+    * v0: Initial version release
+    """
+
+    metadata = {
+        "render_modes": ["human", "rgb_array"],
+        "render_fps": 4,
+    }
+
+    def __init__(self, render_mode: Optional[str] = None, natural=False, sab=False):
+        self.action_space = spaces.Discrete(2)
+        self.observation_space = spaces.Tuple(
+            (spaces.Discrete(32), spaces.Discrete(11), spaces.Discrete(2))
+        )
+
+        # Flag to payout 1.5 on a "natural" blackjack win, like casino rules
+        # Ref: http://www.bicyclecards.com/how-to-play/blackjack/
+        self.natural = natural
+
+        # Flag for full agreement with the (Sutton and Barto, 2018) definition. Overrides self.natural
+        self.sab = sab
+
+        self.render_mode = render_mode
+
+    def step(self, action):
+        assert self.action_space.contains(action)
+        if action:  # hit: add a card to players hand and return
+            self.player.append(draw_card(self.np_random))
+            if is_bust(self.player):
+                terminated = True
+                reward = -1.0
+            else:
+                terminated = False
+                reward = 0.0
+        else:  # stick: play out the dealers hand, and score
+            terminated = True
+            while sum_hand(self.dealer) < 17:
+                self.dealer.append(draw_card(self.np_random))
+            reward = cmp(score(self.player), score(self.dealer))
+            if self.sab and is_natural(self.player) and not is_natural(self.dealer):
+                # Player automatically wins. Rules consistent with S&B
+                reward = 1.0
+            elif (
+                not self.sab
+                and self.natural
+                and is_natural(self.player)
+                and reward == 1.0
+            ):
+                # Natural gives extra points, but doesn't autowin. Legacy implementation
+                reward = 1.5
+
+        if self.render_mode == "human":
+            self.render()
+        return self._get_obs(), reward, terminated, False, {}
+
+    def _get_obs(self):
+        return (sum_hand(self.player), self.dealer[0], usable_ace(self.player))
+
+    def reset(
+        self,
+        seed: Optional[int] = None,
+        options: Optional[dict] = None,
+    ):
+        super().reset(seed=seed)
+        self.dealer = draw_hand(self.np_random)
+        self.player = draw_hand(self.np_random)
+
+        _, dealer_card_value, _ = self._get_obs()
+
+        suits = ["C", "D", "H", "S"]
+        self.dealer_top_card_suit = self.np_random.choice(suits)
+
+        if dealer_card_value == 1:
+            self.dealer_top_card_value_str = "A"
+        elif dealer_card_value == 10:
+            self.dealer_top_card_value_str = self.np_random.choice(["J", "Q", "K"])
+        else:
+            self.dealer_top_card_value_str = str(dealer_card_value)
+
+        if self.render_mode == "human":
+            self.render()
+        return self._get_obs(), {}
+
+    def render(self):
+        if self.render_mode is None:
+            assert self.spec is not None
+            gym.logger.warn(
+                "You are calling render method without specifying any render mode. "
+                "You can specify the render_mode at initialization, "
+                f'e.g. gym.make("{self.spec.id}", render_mode="rgb_array")'
+            )
+            return
+
+        try:
+            import pygame
+        except ImportError as e:
+            raise DependencyNotInstalled(
+                "pygame is not installed, run `pip install gymnasium[toy-text]`"
+            ) from e
+
+        player_sum, dealer_card_value, usable_ace = self._get_obs()
+        screen_width, screen_height = 600, 500
+        card_img_height = screen_height // 3
+        card_img_width = int(card_img_height * 142 / 197)
+        spacing = screen_height // 20
+
+        bg_color = (7, 99, 36)
+        white = (255, 255, 255)
+
+        if not hasattr(self, "screen"):
+            pygame.init()
+            if self.render_mode == "human":
+                pygame.display.init()
+                self.screen = pygame.display.set_mode((screen_width, screen_height))
+            else:
+                pygame.font.init()
+                self.screen = pygame.Surface((screen_width, screen_height))
+
+        if not hasattr(self, "clock"):
+            self.clock = pygame.time.Clock()
+
+        self.screen.fill(bg_color)
+
+        def get_image(path):
+            cwd = os.path.dirname(__file__)
+            image = pygame.image.load(os.path.join(cwd, path))
+            return image
+
+        def get_font(path, size):
+            cwd = os.path.dirname(__file__)
+            font = pygame.font.Font(os.path.join(cwd, path), size)
+            return font
+
+        small_font = get_font(
+            os.path.join("font", "Minecraft.ttf"), screen_height // 15
+        )
+        dealer_text = small_font.render(
+            "Dealer: " + str(dealer_card_value), True, white
+        )
+        dealer_text_rect = self.screen.blit(dealer_text, (spacing, spacing))
+
+        def scale_card_img(card_img):
+            return pygame.transform.scale(card_img, (card_img_width, card_img_height))
+
+        dealer_card_img = scale_card_img(
+            get_image(
+                os.path.join(
+                    "img",
+                    f"{self.dealer_top_card_suit}{self.dealer_top_card_value_str}.png",
+                )
+            )
+        )
+        dealer_card_rect = self.screen.blit(
+            dealer_card_img,
+            (
+                screen_width // 2 - card_img_width - spacing // 2,
+                dealer_text_rect.bottom + spacing,
+            ),
+        )
+
+        hidden_card_img = scale_card_img(get_image(os.path.join("img", "Card.png")))
+        self.screen.blit(
+            hidden_card_img,
+            (
+                screen_width // 2 + spacing // 2,
+                dealer_text_rect.bottom + spacing,
+            ),
+        )
+
+        player_text = small_font.render("Player", True, white)
+        player_text_rect = self.screen.blit(
+            player_text, (spacing, dealer_card_rect.bottom + 1.5 * spacing)
+        )
+
+        large_font = get_font(os.path.join("font", "Minecraft.ttf"), screen_height // 6)
+        player_sum_text = large_font.render(str(player_sum), True, white)
+        player_sum_text_rect = self.screen.blit(
+            player_sum_text,
+            (
+                screen_width // 2 - player_sum_text.get_width() // 2,
+                player_text_rect.bottom + spacing,
+            ),
+        )
+
+        if usable_ace:
+            usable_ace_text = small_font.render("usable ace", True, white)
+            self.screen.blit(
+                usable_ace_text,
+                (
+                    screen_width // 2 - usable_ace_text.get_width() // 2,
+                    player_sum_text_rect.bottom + spacing // 2,
+                ),
+            )
+        if self.render_mode == "human":
+            pygame.event.pump()
+            pygame.display.update()
+            self.clock.tick(self.metadata["render_fps"])
+        else:
+            return np.transpose(
+                np.array(pygame.surfarray.pixels3d(self.screen)), axes=(1, 0, 2)
+            )
+
+    def close(self):
+        if hasattr(self, "screen"):
+            import pygame
+
+            pygame.display.quit()
+            pygame.quit()
+
+
+# Pixel art from Mariia Khmelnytska (https://www.123rf.com/photo_104453049_stock-vector-pixel-art-playing-cards-standart-deck-vector-set.html)

rl/Lib/site-packages/gymnasium/envs/toy_text/cliffwalking.py

@@ -0,0 +1,328 @@
+from contextlib import closing
+from io import StringIO
+from os import path
+from typing import Optional
+
+import numpy as np
+
+import gymnasium as gym
+from gymnasium import Env, spaces
+from gymnasium.envs.toy_text.utils import categorical_sample
+from gymnasium.error import DependencyNotInstalled
+
+
+UP = 0
+RIGHT = 1
+DOWN = 2
+LEFT = 3
+
+
+class CliffWalkingEnv(Env):
+    """
+    Cliff walking involves crossing a gridworld from start to goal while avoiding falling off a cliff.
+
+    ## Description
+    The game starts with the player at location [3, 0] of the 4x12 grid world with the
+    goal located at [3, 11]. If the player reaches the goal the episode ends.
+
+    A cliff runs along [3, 1..10]. If the player moves to a cliff location it
+    returns to the start location.
+
+    The player makes moves until they reach the goal.
+
+    Adapted from Example 6.6 (page 132) from Reinforcement Learning: An Introduction
+    by Sutton and Barto [<a href="#cliffwalk_ref">1</a>].
+
+    With inspiration from:
+    [https://github.com/dennybritz/reinforcement-learning/blob/master/lib/envs/cliff_walking.py](https://github.com/dennybritz/reinforcement-learning/blob/master/lib/envs/cliff_walking.py)
+
+    ## Action Space
+    The action shape is `(1,)` in the range `{0, 3}` indicating
+    which direction to move the player.
+
+    - 0: Move up
+    - 1: Move right
+    - 2: Move down
+    - 3: Move left
+
+    ## Observation Space
+    There are 3 x 12 + 1 possible states. The player cannot be at the cliff, nor at
+    the goal as the latter results in the end of the episode. What remains are all
+    the positions of the first 3 rows plus the bottom-left cell.
+
+    The observation is a value representing the player's current position as
+    current_row * nrows + current_col (where both the row and col start at 0).
+
+    For example, the stating position can be calculated as follows: 3 * 12 + 0 = 36.
+
+    The observation is returned as an `int()`.
+
+    ## Starting State
+    The episode starts with the player in state `[36]` (location [3, 0]).
+
+    ## Reward
+    Each time step incurs -1 reward, unless the player stepped into the cliff,
+    which incurs -100 reward.
+
+    ## Episode End
+    The episode terminates when the player enters state `[47]` (location [3, 11]).
+
+    ## Information
+
+    `step()` and `reset()` return a dict with the following keys:
+    - "p" - transition proability for the state.
+
+    As cliff walking is not stochastic, the transition probability returned always 1.0.
+
+    ## Arguments
+
+    ```python
+    import gymnasium as gym
+    gym.make('CliffWalking-v0')
+    ```
+
+    ## References
+    <a id="cliffwalk_ref"></a>[1] R. Sutton and A. Barto, “Reinforcement Learning:
+    An Introduction” 2020. [Online]. Available: [http://www.incompleteideas.net/book/RLbook2020.pdf](http://www.incompleteideas.net/book/RLbook2020.pdf)
+
+    ## Version History
+    - v0: Initial version release
+
+    """
+
+    metadata = {
+        "render_modes": ["human", "rgb_array", "ansi"],
+        "render_fps": 4,
+    }
+
+    def __init__(self, render_mode: Optional[str] = None):
+        self.shape = (4, 12)
+        self.start_state_index = np.ravel_multi_index((3, 0), self.shape)
+
+        self.nS = np.prod(self.shape)
+        self.nA = 4
+
+        # Cliff Location
+        self._cliff = np.zeros(self.shape, dtype=bool)
+        self._cliff[3, 1:-1] = True
+
+        # Calculate transition probabilities and rewards
+        self.P = {}
+        for s in range(self.nS):
+            position = np.unravel_index(s, self.shape)
+            self.P[s] = {a: [] for a in range(self.nA)}
+            self.P[s][UP] = self._calculate_transition_prob(position, [-1, 0])
+            self.P[s][RIGHT] = self._calculate_transition_prob(position, [0, 1])
+            self.P[s][DOWN] = self._calculate_transition_prob(position, [1, 0])
+            self.P[s][LEFT] = self._calculate_transition_prob(position, [0, -1])
+
+        # Calculate initial state distribution
+        # We always start in state (3, 0)
+        self.initial_state_distrib = np.zeros(self.nS)
+        self.initial_state_distrib[self.start_state_index] = 1.0
+
+        self.observation_space = spaces.Discrete(self.nS)
+        self.action_space = spaces.Discrete(self.nA)
+
+        self.render_mode = render_mode
+
+        # pygame utils
+        self.cell_size = (60, 60)
+        self.window_size = (
+            self.shape[1] * self.cell_size[1],
+            self.shape[0] * self.cell_size[0],
+        )
+        self.window_surface = None
+        self.clock = None
+        self.elf_images = None
+        self.start_img = None
+        self.goal_img = None
+        self.cliff_img = None
+        self.mountain_bg_img = None
+        self.near_cliff_img = None
+        self.tree_img = None
+
+    def _limit_coordinates(self, coord: np.ndarray) -> np.ndarray:
+        """Prevent the agent from falling out of the grid world."""
+        coord[0] = min(coord[0], self.shape[0] - 1)
+        coord[0] = max(coord[0], 0)
+        coord[1] = min(coord[1], self.shape[1] - 1)
+        coord[1] = max(coord[1], 0)
+        return coord
+
+    def _calculate_transition_prob(self, current, delta):
+        """Determine the outcome for an action. Transition Prob is always 1.0.
+
+        Args:
+            current: Current position on the grid as (row, col)
+            delta: Change in position for transition
+
+        Returns:
+            Tuple of ``(1.0, new_state, reward, terminated)``
+        """
+        new_position = np.array(current) + np.array(delta)
+        new_position = self._limit_coordinates(new_position).astype(int)
+        new_state = np.ravel_multi_index(tuple(new_position), self.shape)
+        if self._cliff[tuple(new_position)]:
+            return [(1.0, self.start_state_index, -100, False)]
+
+        terminal_state = (self.shape[0] - 1, self.shape[1] - 1)
+        is_terminated = tuple(new_position) == terminal_state
+        return [(1.0, new_state, -1, is_terminated)]
+
+    def step(self, a):
+        transitions = self.P[self.s][a]
+        i = categorical_sample([t[0] for t in transitions], self.np_random)
+        p, s, r, t = transitions[i]
+        self.s = s
+        self.lastaction = a
+
+        if self.render_mode == "human":
+            self.render()
+        return (int(s), r, t, False, {"prob": p})
+
+    def reset(self, *, seed: Optional[int] = None, options: Optional[dict] = None):
+        super().reset(seed=seed)
+        self.s = categorical_sample(self.initial_state_distrib, self.np_random)
+        self.lastaction = None
+
+        if self.render_mode == "human":
+            self.render()
+        return int(self.s), {"prob": 1}
+
+    def render(self):
+        if self.render_mode is None:
+            assert self.spec is not None
+            gym.logger.warn(
+                "You are calling render method without specifying any render mode. "
+                "You can specify the render_mode at initialization, "
+                f'e.g. gym.make("{self.spec.id}", render_mode="rgb_array")'
+            )
+            return
+
+        if self.render_mode == "ansi":
+            return self._render_text()
+        else:
+            return self._render_gui(self.render_mode)
+
+    def _render_gui(self, mode):
+        try:
+            import pygame
+        except ImportError as e:
+            raise DependencyNotInstalled(
+                "pygame is not installed, run `pip install gymnasium[toy-text]`"
+            ) from e
+        if self.window_surface is None:
+            pygame.init()
+
+            if mode == "human":
+                pygame.display.init()
+                pygame.display.set_caption("CliffWalking")
+                self.window_surface = pygame.display.set_mode(self.window_size)
+            else:  # rgb_array
+                self.window_surface = pygame.Surface(self.window_size)
+        if self.clock is None:
+            self.clock = pygame.time.Clock()
+        if self.elf_images is None:
+            hikers = [
+                path.join(path.dirname(__file__), "img/elf_up.png"),
+                path.join(path.dirname(__file__), "img/elf_right.png"),
+                path.join(path.dirname(__file__), "img/elf_down.png"),
+                path.join(path.dirname(__file__), "img/elf_left.png"),
+            ]
+            self.elf_images = [
+                pygame.transform.scale(pygame.image.load(f_name), self.cell_size)
+                for f_name in hikers
+            ]
+        if self.start_img is None:
+            file_name = path.join(path.dirname(__file__), "img/stool.png")
+            self.start_img = pygame.transform.scale(
+                pygame.image.load(file_name), self.cell_size
+            )
+        if self.goal_img is None:
+            file_name = path.join(path.dirname(__file__), "img/cookie.png")
+            self.goal_img = pygame.transform.scale(
+                pygame.image.load(file_name), self.cell_size
+            )
+        if self.mountain_bg_img is None:
+            bg_imgs = [
+                path.join(path.dirname(__file__), "img/mountain_bg1.png"),
+                path.join(path.dirname(__file__), "img/mountain_bg2.png"),
+            ]
+            self.mountain_bg_img = [
+                pygame.transform.scale(pygame.image.load(f_name), self.cell_size)
+                for f_name in bg_imgs
+            ]
+        if self.near_cliff_img is None:
+            near_cliff_imgs = [
+                path.join(path.dirname(__file__), "img/mountain_near-cliff1.png"),
+                path.join(path.dirname(__file__), "img/mountain_near-cliff2.png"),
+            ]
+            self.near_cliff_img = [
+                pygame.transform.scale(pygame.image.load(f_name), self.cell_size)
+                for f_name in near_cliff_imgs
+            ]
+        if self.cliff_img is None:
+            file_name = path.join(path.dirname(__file__), "img/mountain_cliff.png")
+            self.cliff_img = pygame.transform.scale(
+                pygame.image.load(file_name), self.cell_size
+            )
+
+        for s in range(self.nS):
+            row, col = np.unravel_index(s, self.shape)
+            pos = (col * self.cell_size[0], row * self.cell_size[1])
+            check_board_mask = row % 2 ^ col % 2
+            self.window_surface.blit(self.mountain_bg_img[check_board_mask], pos)
+
+            if self._cliff[row, col]:
+                self.window_surface.blit(self.cliff_img, pos)
+            if row < self.shape[0] - 1 and self._cliff[row + 1, col]:
+                self.window_surface.blit(self.near_cliff_img[check_board_mask], pos)
+            if s == self.start_state_index:
+                self.window_surface.blit(self.start_img, pos)
+            if s == self.nS - 1:
+                self.window_surface.blit(self.goal_img, pos)
+            if s == self.s:
+                elf_pos = (pos[0], pos[1] - 0.1 * self.cell_size[1])
+                last_action = self.lastaction if self.lastaction is not None else 2
+                self.window_surface.blit(self.elf_images[last_action], elf_pos)
+
+        if mode == "human":
+            pygame.event.pump()
+            pygame.display.update()
+            self.clock.tick(self.metadata["render_fps"])
+        else:  # rgb_array
+            return np.transpose(
+                np.array(pygame.surfarray.pixels3d(self.window_surface)), axes=(1, 0, 2)
+            )
+
+    def _render_text(self):
+        outfile = StringIO()
+
+        for s in range(self.nS):
+            position = np.unravel_index(s, self.shape)
+            if self.s == s:
+                output = " x "
+            # Print terminal state
+            elif position == (3, 11):
+                output = " T "
+            elif self._cliff[position]:
+                output = " C "
+            else:
+                output = " o "
+
+            if position[1] == 0:
+                output = output.lstrip()
+            if position[1] == self.shape[1] - 1:
+                output = output.rstrip()
+                output += "\n"
+
+            outfile.write(output)
+        outfile.write("\n")
+
+        with closing(outfile):
+            return outfile.getvalue()
+
+
+# Elf and stool from https://franuka.itch.io/rpg-snow-tileset
+# All other assets by ____

rl/Lib/site-packages/gymnasium/envs/toy_text/font/Minecraft.ttf.import

@@ -0,0 +1,34 @@
+[remap]
+
+importer="font_data_dynamic"
+type="FontFile"
+uid="uid://chhfbqha83p6k"
+path="res://.godot/imported/Minecraft.ttf-f9e8e494004917bf3ed58bc874dca03f.fontdata"
+
+[deps]
+
+source_file="res://rl/Lib/site-packages/gymnasium/envs/toy_text/font/Minecraft.ttf"
+dest_files=["res://.godot/imported/Minecraft.ttf-f9e8e494004917bf3ed58bc874dca03f.fontdata"]
+
+[params]
+
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+antialiasing=1
+generate_mipmaps=false
+disable_embedded_bitmaps=true
+multichannel_signed_distance_field=false
+msdf_pixel_range=8
+msdf_size=48
+allow_system_fallback=true
+force_autohinter=false
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+subpixel_positioning=1
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+compress=true
+preload=[]
+language_support={}
+script_support={}
+opentype_features={}

rl/Lib/site-packages/gymnasium/envs/toy_text/frozen_lake.py

@@ -0,0 +1,472 @@
+from contextlib import closing
+from io import StringIO
+from os import path
+from typing import List, Optional
+
+import numpy as np
+
+import gymnasium as gym
+from gymnasium import Env, spaces, utils
+from gymnasium.envs.toy_text.utils import categorical_sample
+from gymnasium.error import DependencyNotInstalled
+from gymnasium.utils import seeding
+
+
+LEFT = 0
+DOWN = 1
+RIGHT = 2
+UP = 3
+
+MAPS = {
+    "4x4": ["SFFF", "FHFH", "FFFH", "HFFG"],
+    "8x8": [
+        "SFFFFFFF",
+        "FFFFFFFF",
+        "FFFHFFFF",
+        "FFFFFHFF",
+        "FFFHFFFF",
+        "FHHFFFHF",
+        "FHFFHFHF",
+        "FFFHFFFG",
+    ],
+}
+
+
+# DFS to check that it's a valid path.
+def is_valid(board: List[List[str]], max_size: int) -> bool:
+    frontier, discovered = [], set()
+    frontier.append((0, 0))
+    while frontier:
+        r, c = frontier.pop()
+        if not (r, c) in discovered:
+            discovered.add((r, c))
+            directions = [(1, 0), (0, 1), (-1, 0), (0, -1)]
+            for x, y in directions:
+                r_new = r + x
+                c_new = c + y
+                if r_new < 0 or r_new >= max_size or c_new < 0 or c_new >= max_size:
+                    continue
+                if board[r_new][c_new] == "G":
+                    return True
+                if board[r_new][c_new] != "H":
+                    frontier.append((r_new, c_new))
+    return False
+
+
+def generate_random_map(
+    size: int = 8, p: float = 0.8, seed: Optional[int] = None
+) -> List[str]:
+    """Generates a random valid map (one that has a path from start to goal)
+
+    Args:
+        size: size of each side of the grid
+        p: probability that a tile is frozen
+        seed: optional seed to ensure the generation of reproducible maps
+
+    Returns:
+        A random valid map
+    """
+    valid = False
+    board = []  # initialize to make pyright happy
+
+    np_random, _ = seeding.np_random(seed)
+
+    while not valid:
+        p = min(1, p)
+        board = np_random.choice(["F", "H"], (size, size), p=[p, 1 - p])
+        board[0][0] = "S"
+        board[-1][-1] = "G"
+        valid = is_valid(board, size)
+    return ["".join(x) for x in board]
+
+
+class FrozenLakeEnv(Env):
+    """
+    Frozen lake involves crossing a frozen lake from start to goal without falling into any holes
+    by walking over the frozen lake.
+    The player may not always move in the intended direction due to the slippery nature of the frozen lake.
+
+    ## Description
+    The game starts with the player at location [0,0] of the frozen lake grid world with the
+    goal located at far extent of the world e.g. [3,3] for the 4x4 environment.
+
+    Holes in the ice are distributed in set locations when using a pre-determined map
+    or in random locations when a random map is generated.
+
+    The player makes moves until they reach the goal or fall in a hole.
+
+    The lake is slippery (unless disabled) so the player may move perpendicular
+    to the intended direction sometimes (see <a href="#is_slippy">`is_slippery`</a>).
+
+    Randomly generated worlds will always have a path to the goal.
+
+    Elf and stool from [https://franuka.itch.io/rpg-snow-tileset](https://franuka.itch.io/rpg-snow-tileset).
+    All other assets by Mel Tillery [http://www.cyaneus.com/](http://www.cyaneus.com/).
+
+    ## Action Space
+    The action shape is `(1,)` in the range `{0, 3}` indicating
+    which direction to move the player.
+
+    - 0: Move left
+    - 1: Move down
+    - 2: Move right
+    - 3: Move up
+
+    ## Observation Space
+    The observation is a value representing the player's current position as
+    current_row * nrows + current_col (where both the row and col start at 0).
+
+    For example, the goal position in the 4x4 map can be calculated as follows: 3 * 4 + 3 = 15.
+    The number of possible observations is dependent on the size of the map.
+
+    The observation is returned as an `int()`.
+
+    ## Starting State
+    The episode starts with the player in state `[0]` (location [0, 0]).
+
+    ## Rewards
+
+    Reward schedule:
+    - Reach goal: +1
+    - Reach hole: 0
+    - Reach frozen: 0
+
+    ## Episode End
+    The episode ends if the following happens:
+
+    - Termination:
+        1. The player moves into a hole.
+        2. The player reaches the goal at `max(nrow) * max(ncol) - 1` (location `[max(nrow)-1, max(ncol)-1]`).
+
+    - Truncation (when using the time_limit wrapper):
+        1. The length of the episode is 100 for 4x4 environment, 200 for FrozenLake8x8-v1 environment.
+
+    ## Information
+
+    `step()` and `reset()` return a dict with the following keys:
+    - p - transition probability for the state.
+
+    See <a href="#is_slippy">`is_slippery`</a> for transition probability information.
+
+
+    ## Arguments
+
+    ```python
+    import gymnasium as gym
+    gym.make('FrozenLake-v1', desc=None, map_name="4x4", is_slippery=True)
+    ```
+
+    `desc=None`: Used to specify maps non-preloaded maps.
+
+    Specify a custom map.
+    ```
+        desc=["SFFF", "FHFH", "FFFH", "HFFG"].
+    ```
+
+    A random generated map can be specified by calling the function `generate_random_map`.
+    ```
+    from gymnasium.envs.toy_text.frozen_lake import generate_random_map
+
+    gym.make('FrozenLake-v1', desc=generate_random_map(size=8))
+    ```
+
+    `map_name="4x4"`: ID to use any of the preloaded maps.
+    ```
+        "4x4":[
+            "SFFF",
+            "FHFH",
+            "FFFH",
+            "HFFG"
+            ]
+
+        "8x8": [
+            "SFFFFFFF",
+            "FFFFFFFF",
+            "FFFHFFFF",
+            "FFFFFHFF",
+            "FFFHFFFF",
+            "FHHFFFHF",
+            "FHFFHFHF",
+            "FFFHFFFG",
+        ]
+    ```
+
+    If `desc=None` then `map_name` will be used. If both `desc` and `map_name` are
+    `None` a random 8x8 map with 80% of locations frozen will be generated.
+
+    <a id="is_slippy"></a>`is_slippery=True`: If true the player will move in intended direction with
+    probability of 1/3 else will move in either perpendicular direction with
+    equal probability of 1/3 in both directions.
+
+    For example, if action is left and is_slippery is True, then:
+    - P(move left)=1/3
+    - P(move up)=1/3
+    - P(move down)=1/3
+
+
+    ## Version History
+    * v1: Bug fixes to rewards
+    * v0: Initial version release
+
+    """
+
+    metadata = {
+        "render_modes": ["human", "ansi", "rgb_array"],
+        "render_fps": 4,
+    }
+
+    def __init__(
+        self,
+        render_mode: Optional[str] = None,
+        desc=None,
+        map_name="4x4",
+        is_slippery=True,
+    ):
+        if desc is None and map_name is None:
+            desc = generate_random_map()
+        elif desc is None:
+            desc = MAPS[map_name]
+        self.desc = desc = np.asarray(desc, dtype="c")
+        self.nrow, self.ncol = nrow, ncol = desc.shape
+        self.reward_range = (0, 1)
+
+        nA = 4
+        nS = nrow * ncol
+
+        self.initial_state_distrib = np.array(desc == b"S").astype("float64").ravel()
+        self.initial_state_distrib /= self.initial_state_distrib.sum()
+
+        self.P = {s: {a: [] for a in range(nA)} for s in range(nS)}
+
+        def to_s(row, col):
+            return row * ncol + col
+
+        def inc(row, col, a):
+            if a == LEFT:
+                col = max(col - 1, 0)
+            elif a == DOWN:
+                row = min(row + 1, nrow - 1)
+            elif a == RIGHT:
+                col = min(col + 1, ncol - 1)
+            elif a == UP:
+                row = max(row - 1, 0)
+            return (row, col)
+
+        def update_probability_matrix(row, col, action):
+            newrow, newcol = inc(row, col, action)
+            newstate = to_s(newrow, newcol)
+            newletter = desc[newrow, newcol]
+            terminated = bytes(newletter) in b"GH"
+            reward = float(newletter == b"G")
+            return newstate, reward, terminated
+
+        for row in range(nrow):
+            for col in range(ncol):
+                s = to_s(row, col)
+                for a in range(4):
+                    li = self.P[s][a]
+                    letter = desc[row, col]
+                    if letter in b"GH":
+                        li.append((1.0, s, 0, True))
+                    else:
+                        if is_slippery:
+                            for b in [(a - 1) % 4, a, (a + 1) % 4]:
+                                li.append(
+                                    (1.0 / 3.0, *update_probability_matrix(row, col, b))
+                                )
+                        else:
+                            li.append((1.0, *update_probability_matrix(row, col, a)))
+
+        self.observation_space = spaces.Discrete(nS)
+        self.action_space = spaces.Discrete(nA)
+
+        self.render_mode = render_mode
+
+        # pygame utils
+        self.window_size = (min(64 * ncol, 512), min(64 * nrow, 512))
+        self.cell_size = (
+            self.window_size[0] // self.ncol,
+            self.window_size[1] // self.nrow,
+        )
+        self.window_surface = None
+        self.clock = None
+        self.hole_img = None
+        self.cracked_hole_img = None
+        self.ice_img = None
+        self.elf_images = None
+        self.goal_img = None
+        self.start_img = None
+
+    def step(self, a):
+        transitions = self.P[self.s][a]
+        i = categorical_sample([t[0] for t in transitions], self.np_random)
+        p, s, r, t = transitions[i]
+        self.s = s
+        self.lastaction = a
+
+        if self.render_mode == "human":
+            self.render()
+        return (int(s), r, t, False, {"prob": p})
+
+    def reset(
+        self,
+        *,
+        seed: Optional[int] = None,
+        options: Optional[dict] = None,
+    ):
+        super().reset(seed=seed)
+        self.s = categorical_sample(self.initial_state_distrib, self.np_random)
+        self.lastaction = None
+
+        if self.render_mode == "human":
+            self.render()
+        return int(self.s), {"prob": 1}
+
+    def render(self):
+        if self.render_mode is None:
+            assert self.spec is not None
+            gym.logger.warn(
+                "You are calling render method without specifying any render mode. "
+                "You can specify the render_mode at initialization, "
+                f'e.g. gym.make("{self.spec.id}", render_mode="rgb_array")'
+            )
+            return
+
+        if self.render_mode == "ansi":
+            return self._render_text()
+        else:  # self.render_mode in {"human", "rgb_array"}:
+            return self._render_gui(self.render_mode)
+
+    def _render_gui(self, mode):
+        try:
+            import pygame
+        except ImportError as e:
+            raise DependencyNotInstalled(
+                "pygame is not installed, run `pip install gymnasium[toy-text]`"
+            ) from e
+
+        if self.window_surface is None:
+            pygame.init()
+
+            if mode == "human":
+                pygame.display.init()
+                pygame.display.set_caption("Frozen Lake")
+                self.window_surface = pygame.display.set_mode(self.window_size)
+            elif mode == "rgb_array":
+                self.window_surface = pygame.Surface(self.window_size)
+
+        assert (
+            self.window_surface is not None
+        ), "Something went wrong with pygame. This should never happen."
+
+        if self.clock is None:
+            self.clock = pygame.time.Clock()
+        if self.hole_img is None:
+            file_name = path.join(path.dirname(__file__), "img/hole.png")
+            self.hole_img = pygame.transform.scale(
+                pygame.image.load(file_name), self.cell_size
+            )
+        if self.cracked_hole_img is None:
+            file_name = path.join(path.dirname(__file__), "img/cracked_hole.png")
+            self.cracked_hole_img = pygame.transform.scale(
+                pygame.image.load(file_name), self.cell_size
+            )
+        if self.ice_img is None:
+            file_name = path.join(path.dirname(__file__), "img/ice.png")
+            self.ice_img = pygame.transform.scale(
+                pygame.image.load(file_name), self.cell_size
+            )
+        if self.goal_img is None:
+            file_name = path.join(path.dirname(__file__), "img/goal.png")
+            self.goal_img = pygame.transform.scale(
+                pygame.image.load(file_name), self.cell_size
+            )
+        if self.start_img is None:
+            file_name = path.join(path.dirname(__file__), "img/stool.png")
+            self.start_img = pygame.transform.scale(
+                pygame.image.load(file_name), self.cell_size
+            )
+        if self.elf_images is None:
+            elfs = [
+                path.join(path.dirname(__file__), "img/elf_left.png"),
+                path.join(path.dirname(__file__), "img/elf_down.png"),
+                path.join(path.dirname(__file__), "img/elf_right.png"),
+                path.join(path.dirname(__file__), "img/elf_up.png"),
+            ]
+            self.elf_images = [
+                pygame.transform.scale(pygame.image.load(f_name), self.cell_size)
+                for f_name in elfs
+            ]
+
+        desc = self.desc.tolist()
+        assert isinstance(desc, list), f"desc should be a list or an array, got {desc}"
+        for y in range(self.nrow):
+            for x in range(self.ncol):
+                pos = (x * self.cell_size[0], y * self.cell_size[1])
+                rect = (*pos, *self.cell_size)
+
+                self.window_surface.blit(self.ice_img, pos)
+                if desc[y][x] == b"H":
+                    self.window_surface.blit(self.hole_img, pos)
+                elif desc[y][x] == b"G":
+                    self.window_surface.blit(self.goal_img, pos)
+                elif desc[y][x] == b"S":
+                    self.window_surface.blit(self.start_img, pos)
+
+                pygame.draw.rect(self.window_surface, (180, 200, 230), rect, 1)
+
+        # paint the elf
+        bot_row, bot_col = self.s // self.ncol, self.s % self.ncol
+        cell_rect = (bot_col * self.cell_size[0], bot_row * self.cell_size[1])
+        last_action = self.lastaction if self.lastaction is not None else 1
+        elf_img = self.elf_images[last_action]
+
+        if desc[bot_row][bot_col] == b"H":
+            self.window_surface.blit(self.cracked_hole_img, cell_rect)
+        else:
+            self.window_surface.blit(elf_img, cell_rect)
+
+        if mode == "human":
+            pygame.event.pump()
+            pygame.display.update()
+            self.clock.tick(self.metadata["render_fps"])
+        elif mode == "rgb_array":
+            return np.transpose(
+                np.array(pygame.surfarray.pixels3d(self.window_surface)), axes=(1, 0, 2)
+            )
+
+    @staticmethod
+    def _center_small_rect(big_rect, small_dims):
+        offset_w = (big_rect[2] - small_dims[0]) / 2
+        offset_h = (big_rect[3] - small_dims[1]) / 2
+        return (
+            big_rect[0] + offset_w,
+            big_rect[1] + offset_h,
+        )
+
+    def _render_text(self):
+        desc = self.desc.tolist()
+        outfile = StringIO()
+
+        row, col = self.s // self.ncol, self.s % self.ncol
+        desc = [[c.decode("utf-8") for c in line] for line in desc]
+        desc[row][col] = utils.colorize(desc[row][col], "red", highlight=True)
+        if self.lastaction is not None:
+            outfile.write(f"  ({['Left', 'Down', 'Right', 'Up'][self.lastaction]})\n")
+        else:
+            outfile.write("\n")
+        outfile.write("\n".join("".join(line) for line in desc) + "\n")
+
+        with closing(outfile):
+            return outfile.getvalue()
+
+    def close(self):
+        if self.window_surface is not None:
+            import pygame
+
+            pygame.display.quit()
+            pygame.quit()
+
+
+# Elf and stool from https://franuka.itch.io/rpg-snow-tileset
+# All other assets by Mel Tillery http://www.cyaneus.com/

rl/Lib/site-packages/gymnasium/envs/toy_text/img/C2.png.import

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