MLPY/Lib/site-packages/gym/envs/toy_text/taxi.py

from contextlib import closing
from io import StringIO
from os import path
from typing import Optional

import numpy as np

from gym import Env, logger, spaces, utils
from gym.envs.toy_text.utils import categorical_sample
from gym.error import DependencyNotInstalled

MAP = [
    "+---------+",
    "|R: | : :G|",
    "| : | : : |",
    "| : : : : |",
    "| | : | : |",
    "|Y| : |B: |",
    "+---------+",
]
WINDOW_SIZE = (550, 350)


class TaxiEnv(Env):
    """

    The Taxi Problem
    from "Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition"
    by Tom Dietterich

    ### Description
    There are four designated locations in the grid world indicated by R(ed),
    G(reen), Y(ellow), and B(lue). When the episode starts, the taxi starts off
    at a random square and the passenger is at a random location. The taxi
    drives to the passenger's location, picks up the passenger, drives to the
    passenger's destination (another one of the four specified locations), and
    then drops off the passenger. Once the passenger is dropped off, the episode ends.

    Map:

        +---------+
        |R: | : :G|
        | : | : : |
        | : : : : |
        | | : | : |
        |Y| : |B: |
        +---------+

    ### Actions
    There are 6 discrete deterministic actions:
    - 0: move south
    - 1: move north
    - 2: move east
    - 3: move west
    - 4: pickup passenger
    - 5: drop off passenger

    ### Observations
    There are 500 discrete states since there are 25 taxi positions, 5 possible
    locations of the passenger (including the case when the passenger is in the
    taxi), and 4 destination locations.

    Note that there are 400 states that can actually be reached during an
    episode. The missing states correspond to situations in which the passenger
    is at the same location as their destination, as this typically signals the
    end of an episode. Four additional states can be observed right after a
    successful episodes, when both the passenger and the taxi are at the destination.
    This gives a total of 404 reachable discrete states.

    Each state space is represented by the tuple:
    (taxi_row, taxi_col, passenger_location, destination)

    An observation is an integer that encodes the corresponding state.
    The state tuple can then be decoded with the "decode" method.

    Passenger locations:
    - 0: R(ed)
    - 1: G(reen)
    - 2: Y(ellow)
    - 3: B(lue)
    - 4: in taxi

    Destinations:
    - 0: R(ed)
    - 1: G(reen)
    - 2: Y(ellow)
    - 3: B(lue)

    ### Info

    ``step`` and ``reset()`` will return an info dictionary that contains "p" and "action_mask" containing
        the probability that the state is taken and a mask of what actions will result in a change of state to speed up training.

    As Taxi's initial state is a stochastic, the "p" key represents the probability of the
    transition however this value is currently bugged being 1.0, this will be fixed soon.
    As the steps are deterministic, "p" represents the probability of the transition which is always 1.0

    For some cases, taking an action will have no effect on the state of the agent.
    In v0.25.0, ``info["action_mask"]`` contains a np.ndarray for each of the action specifying
    if the action will change the state.

    To sample a modifying action, use ``action = env.action_space.sample(info["action_mask"])``
    Or with a Q-value based algorithm ``action = np.argmax(q_values[obs, np.where(info["action_mask"] == 1)[0]])``.

    ### Rewards
    - -1 per step unless other reward is triggered.
    - +20 delivering passenger.
    - -10  executing "pickup" and "drop-off" actions illegally.

    ### Arguments

    ```
    gym.make('Taxi-v3')
    ```

    ### Version History
    * v3: Map Correction + Cleaner Domain Description, v0.25.0 action masking added to the reset and step information
    * v2: Disallow Taxi start location = goal location, Update Taxi observations in the rollout, Update Taxi reward threshold.
    * v1: Remove (3,2) from locs, add passidx<4 check
    * v0: Initial versions release
    """

    metadata = {
        "render_modes": ["human", "ansi", "rgb_array"],
        "render_fps": 4,
    }

    def __init__(self, render_mode: Optional[str] = None):
        self.desc = np.asarray(MAP, dtype="c")

        self.locs = locs = [(0, 0), (0, 4), (4, 0), (4, 3)]
        self.locs_colors = [(255, 0, 0), (0, 255, 0), (255, 255, 0), (0, 0, 255)]

        num_states = 500
        num_rows = 5
        num_columns = 5
        max_row = num_rows - 1
        max_col = num_columns - 1
        self.initial_state_distrib = np.zeros(num_states)
        num_actions = 6
        self.P = {
            state: {action: [] for action in range(num_actions)}
            for state in range(num_states)
        }
        for row in range(num_rows):
            for col in range(num_columns):
                for pass_idx in range(len(locs) + 1):  # +1 for being inside taxi
                    for dest_idx in range(len(locs)):
                        state = self.encode(row, col, pass_idx, dest_idx)
                        if pass_idx < 4 and pass_idx != dest_idx:
                            self.initial_state_distrib[state] += 1
                        for action in range(num_actions):
                            # defaults
                            new_row, new_col, new_pass_idx = row, col, pass_idx
                            reward = (
                                -1
                            )  # default reward when there is no pickup/dropoff
                            terminated = False
                            taxi_loc = (row, col)

                            if action == 0:
                                new_row = min(row + 1, max_row)
                            elif action == 1:
                                new_row = max(row - 1, 0)
                            if action == 2 and self.desc[1 + row, 2 * col + 2] == b":":
                                new_col = min(col + 1, max_col)
                            elif action == 3 and self.desc[1 + row, 2 * col] == b":":
                                new_col = max(col - 1, 0)
                            elif action == 4:  # pickup
                                if pass_idx < 4 and taxi_loc == locs[pass_idx]:
                                    new_pass_idx = 4
                                else:  # passenger not at location
                                    reward = -10
                            elif action == 5:  # dropoff
                                if (taxi_loc == locs[dest_idx]) and pass_idx == 4:
                                    new_pass_idx = dest_idx
                                    terminated = True
                                    reward = 20
                                elif (taxi_loc in locs) and pass_idx == 4:
                                    new_pass_idx = locs.index(taxi_loc)
                                else:  # dropoff at wrong location
                                    reward = -10
                            new_state = self.encode(
                                new_row, new_col, new_pass_idx, dest_idx
                            )
                            self.P[state][action].append(
                                (1.0, new_state, reward, terminated)
                            )
        self.initial_state_distrib /= self.initial_state_distrib.sum()
        self.action_space = spaces.Discrete(num_actions)
        self.observation_space = spaces.Discrete(num_states)

        self.render_mode = render_mode

        # pygame utils
        self.window = None
        self.clock = None
        self.cell_size = (
            WINDOW_SIZE[0] / self.desc.shape[1],
            WINDOW_SIZE[1] / self.desc.shape[0],
        )
        self.taxi_imgs = None
        self.taxi_orientation = 0
        self.passenger_img = None
        self.destination_img = None
        self.median_horiz = None
        self.median_vert = None
        self.background_img = None

    def encode(self, taxi_row, taxi_col, pass_loc, dest_idx):
        # (5) 5, 5, 4
        i = taxi_row
        i *= 5
        i += taxi_col
        i *= 5
        i += pass_loc
        i *= 4
        i += dest_idx
        return i

    def decode(self, i):
        out = []
        out.append(i % 4)
        i = i // 4
        out.append(i % 5)
        i = i // 5
        out.append(i % 5)
        i = i // 5
        out.append(i)
        assert 0 <= i < 5
        return reversed(out)

    def action_mask(self, state: int):
        """Computes an action mask for the action space using the state information."""
        mask = np.zeros(6, dtype=np.int8)
        taxi_row, taxi_col, pass_loc, dest_idx = self.decode(state)
        if taxi_row < 4:
            mask[0] = 1
        if taxi_row > 0:
            mask[1] = 1
        if taxi_col < 4 and self.desc[taxi_row + 1, 2 * taxi_col + 2] == b":":
            mask[2] = 1
        if taxi_col > 0 and self.desc[taxi_row + 1, 2 * taxi_col] == b":":
            mask[3] = 1
        if pass_loc < 4 and (taxi_row, taxi_col) == self.locs[pass_loc]:
            mask[4] = 1
        if pass_loc == 4 and (
            (taxi_row, taxi_col) == self.locs[dest_idx]
            or (taxi_row, taxi_col) in self.locs
        ):
            mask[5] = 1
        return mask

    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, "action_mask": self.action_mask(s)})

    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
        self.taxi_orientation = 0

        if self.render_mode == "human":
            self.render()
        return int(self.s), {"prob": 1.0, "action_mask": self.action_mask(self.s)}

    def render(self):
        if self.render_mode is None:
            logger.warn(
                "You are calling render method without specifying any render mode. "
                "You can specify the render_mode at initialization, "
                f'e.g. gym("{self.spec.id}", render_mode="rgb_array")'
            )
        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  # dependency to pygame only if rendering with human
        except ImportError:
            raise DependencyNotInstalled(
                "pygame is not installed, run `pip install gym[toy_text]`"
            )

        if self.window is None:
            pygame.init()
            pygame.display.set_caption("Taxi")
            if mode == "human":
                self.window = pygame.display.set_mode(WINDOW_SIZE)
            elif mode == "rgb_array":
                self.window = pygame.Surface(WINDOW_SIZE)

        assert (
            self.window is not None
        ), "Something went wrong with pygame. This should never happen."
        if self.clock is None:
            self.clock = pygame.time.Clock()
        if self.taxi_imgs is None:
            file_names = [
                path.join(path.dirname(__file__), "img/cab_front.png"),
                path.join(path.dirname(__file__), "img/cab_rear.png"),
                path.join(path.dirname(__file__), "img/cab_right.png"),
                path.join(path.dirname(__file__), "img/cab_left.png"),
            ]
            self.taxi_imgs = [
                pygame.transform.scale(pygame.image.load(file_name), self.cell_size)
                for file_name in file_names
            ]
        if self.passenger_img is None:
            file_name = path.join(path.dirname(__file__), "img/passenger.png")
            self.passenger_img = pygame.transform.scale(
                pygame.image.load(file_name), self.cell_size
            )
        if self.destination_img is None:
            file_name = path.join(path.dirname(__file__), "img/hotel.png")
            self.destination_img = pygame.transform.scale(
                pygame.image.load(file_name), self.cell_size
            )
            self.destination_img.set_alpha(170)
        if self.median_horiz is None:
            file_names = [
                path.join(path.dirname(__file__), "img/gridworld_median_left.png"),
                path.join(path.dirname(__file__), "img/gridworld_median_horiz.png"),
                path.join(path.dirname(__file__), "img/gridworld_median_right.png"),
            ]
            self.median_horiz = [
                pygame.transform.scale(pygame.image.load(file_name), self.cell_size)
                for file_name in file_names
            ]
        if self.median_vert is None:
            file_names = [
                path.join(path.dirname(__file__), "img/gridworld_median_top.png"),
                path.join(path.dirname(__file__), "img/gridworld_median_vert.png"),
                path.join(path.dirname(__file__), "img/gridworld_median_bottom.png"),
            ]
            self.median_vert = [
                pygame.transform.scale(pygame.image.load(file_name), self.cell_size)
                for file_name in file_names
            ]
        if self.background_img is None:
            file_name = path.join(path.dirname(__file__), "img/taxi_background.png")
            self.background_img = pygame.transform.scale(
                pygame.image.load(file_name), self.cell_size
            )

        desc = self.desc

        for y in range(0, desc.shape[0]):
            for x in range(0, desc.shape[1]):
                cell = (x * self.cell_size[0], y * self.cell_size[1])
                self.window.blit(self.background_img, cell)
                if desc[y][x] == b"|" and (y == 0 or desc[y - 1][x] != b"|"):
                    self.window.blit(self.median_vert[0], cell)
                elif desc[y][x] == b"|" and (
                    y == desc.shape[0] - 1 or desc[y + 1][x] != b"|"
                ):
                    self.window.blit(self.median_vert[2], cell)
                elif desc[y][x] == b"|":
                    self.window.blit(self.median_vert[1], cell)
                elif desc[y][x] == b"-" and (x == 0 or desc[y][x - 1] != b"-"):
                    self.window.blit(self.median_horiz[0], cell)
                elif desc[y][x] == b"-" and (
                    x == desc.shape[1] - 1 or desc[y][x + 1] != b"-"
                ):
                    self.window.blit(self.median_horiz[2], cell)
                elif desc[y][x] == b"-":
                    self.window.blit(self.median_horiz[1], cell)

        for cell, color in zip(self.locs, self.locs_colors):
            color_cell = pygame.Surface(self.cell_size)
            color_cell.set_alpha(128)
            color_cell.fill(color)
            loc = self.get_surf_loc(cell)
            self.window.blit(color_cell, (loc[0], loc[1] + 10))

        taxi_row, taxi_col, pass_idx, dest_idx = self.decode(self.s)

        if pass_idx < 4:
            self.window.blit(self.passenger_img, self.get_surf_loc(self.locs[pass_idx]))

        if self.lastaction in [0, 1, 2, 3]:
            self.taxi_orientation = self.lastaction
        dest_loc = self.get_surf_loc(self.locs[dest_idx])
        taxi_location = self.get_surf_loc((taxi_row, taxi_col))

        if dest_loc[1] <= taxi_location[1]:
            self.window.blit(
                self.destination_img,
                (dest_loc[0], dest_loc[1] - self.cell_size[1] // 2),
            )
            self.window.blit(self.taxi_imgs[self.taxi_orientation], taxi_location)
        else:  # change blit order for overlapping appearance
            self.window.blit(self.taxi_imgs[self.taxi_orientation], taxi_location)
            self.window.blit(
                self.destination_img,
                (dest_loc[0], dest_loc[1] - self.cell_size[1] // 2),
            )

        if mode == "human":
            pygame.display.update()
            self.clock.tick(self.metadata["render_fps"])
        elif mode == "rgb_array":
            return np.transpose(
                np.array(pygame.surfarray.pixels3d(self.window)), axes=(1, 0, 2)
            )

    def get_surf_loc(self, map_loc):
        return (map_loc[1] * 2 + 1) * self.cell_size[0], (
            map_loc[0] + 1
        ) * self.cell_size[1]

    def _render_text(self):
        desc = self.desc.copy().tolist()
        outfile = StringIO()

        out = [[c.decode("utf-8") for c in line] for line in desc]
        taxi_row, taxi_col, pass_idx, dest_idx = self.decode(self.s)

        def ul(x):
            return "_" if x == " " else x

        if pass_idx < 4:
            out[1 + taxi_row][2 * taxi_col + 1] = utils.colorize(
                out[1 + taxi_row][2 * taxi_col + 1], "yellow", highlight=True
            )
            pi, pj = self.locs[pass_idx]
            out[1 + pi][2 * pj + 1] = utils.colorize(
                out[1 + pi][2 * pj + 1], "blue", bold=True
            )
        else:  # passenger in taxi
            out[1 + taxi_row][2 * taxi_col + 1] = utils.colorize(
                ul(out[1 + taxi_row][2 * taxi_col + 1]), "green", highlight=True
            )

        di, dj = self.locs[dest_idx]
        out[1 + di][2 * dj + 1] = utils.colorize(out[1 + di][2 * dj + 1], "magenta")
        outfile.write("\n".join(["".join(row) for row in out]) + "\n")
        if self.lastaction is not None:
            outfile.write(
                f"  ({['South', 'North', 'East', 'West', 'Pickup', 'Dropoff'][self.lastaction]})\n"
            )
        else:
            outfile.write("\n")

        with closing(outfile):
            return outfile.getvalue()

    def close(self):
        if self.window is not None:
            import pygame

            pygame.display.quit()
            pygame.quit()


# Taxi rider from https://franuka.itch.io/rpg-asset-pack
# All other assets by Mel Tillery http://www.cyaneus.com/