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

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 ____