Gymnasium/gym/envs/unittest/cube_crash.py

from typing import Optional

import numpy as np
import gym
from gym import spaces
from gym.utils import seeding

# Unit test environment for CNNs and CNN+RNN algorithms.
# Looks like this (RGB observations):
#
#  ---------------------------
# |                           |
# |                           |
# |                           |
# |          **               |
# |          **               |
# |                           |
# |                           |
# |                           |
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#  ========     ==============
#
# Goal is to go through the hole at the bottom. Agent controls square using Left-Nop-Right actions.
# It falls down automatically, episode length is a bit less than FIELD_H
#
# CubeCrash-v0                    # shaped reward
# CubeCrashSparse-v0              # reward 0 or 1 at the end
# CubeCrashScreenBecomesBlack-v0  # for RNNs
#
# To see how it works, run:
#
# python examples/agents/keyboard_agent.py CubeCrashScreen-v0

FIELD_W = 32
FIELD_H = 40
HOLE_WIDTH = 8

color_black = np.array((0, 0, 0)).astype("float32")
color_white = np.array((255, 255, 255)).astype("float32")
color_green = np.array((0, 255, 0)).astype("float32")


class CubeCrash(gym.Env):
    metadata = {
        "render.modes": ["human", "rgb_array"],
        "video.frames_per_second": 60,
        "video.res_w": FIELD_W,
        "video.res_h": FIELD_H,
    }

    use_shaped_reward = True
    use_black_screen = False
    use_random_colors = False  # Makes env too hard

    def __init__(self):
        self.viewer = None

        self.observation_space = spaces.Box(
            0, 255, (FIELD_H, FIELD_W, 3), dtype=np.uint8
        )
        self.action_space = spaces.Discrete(3)

        self.reset()

    def random_color(self):
        return np.array(
            [
                self.np_random.integers(low=0, high=255),
                self.np_random.integers(low=0, high=255),
                self.np_random.integers(low=0, high=255),
            ]
        ).astype("uint8")

    def reset(self, seed: Optional[int] = None):
        super().reset(seed=seed)
        self.cube_x = self.np_random.integers(low=3, high=FIELD_W - 3)
        self.cube_y = self.np_random.integers(low=3, high=FIELD_H // 6)
        self.hole_x = self.np_random.integers(low=HOLE_WIDTH, high=FIELD_W - HOLE_WIDTH)
        self.bg_color = self.random_color() if self.use_random_colors else color_black
        self.potential = None
        self.step_n = 0
        while 1:
            self.wall_color = (
                self.random_color() if self.use_random_colors else color_white
            )
            self.cube_color = (
                self.random_color() if self.use_random_colors else color_green
            )
            if (
                np.linalg.norm(self.wall_color - self.bg_color) < 50
                or np.linalg.norm(self.cube_color - self.bg_color) < 50
            ):
                continue
            break

        return self.step(0)[0]

    def step(self, action):
        if action == 0:
            pass
        elif action == 1:
            self.cube_x -= 1
        elif action == 2:
            self.cube_x += 1
        else:
            assert 0, "Action %i is out of range" % action
        self.cube_y += 1
        self.step_n += 1

        obs = np.zeros((FIELD_H, FIELD_W, 3), dtype=np.uint8)
        obs[:, :, :] = self.bg_color
        obs[FIELD_H - 5 : FIELD_H, :, :] = self.wall_color
        obs[
            FIELD_H - 5 : FIELD_H,
            self.hole_x - HOLE_WIDTH // 2 : self.hole_x + HOLE_WIDTH // 2 + 1,
            :,
        ] = self.bg_color
        obs[
            self.cube_y - 1 : self.cube_y + 2, self.cube_x - 1 : self.cube_x + 2, :
        ] = self.cube_color
        if self.use_black_screen and self.step_n > 4:
            obs[:] = np.zeros((3,), dtype=np.uint8)

        done = False
        reward = 0
        dist = np.abs(self.cube_x - self.hole_x)
        if self.potential is not None and self.use_shaped_reward:
            reward = (self.potential - dist) * 0.01
        self.potential = dist

        if self.cube_x - 1 < 0 or self.cube_x + 1 >= FIELD_W:
            done = True
            reward = -1
        elif self.cube_y + 1 >= FIELD_H - 5:
            if dist >= HOLE_WIDTH // 2:
                done = True
                reward = -1
            elif self.cube_y == FIELD_H:
                done = True
                reward = +1
        self.last_obs = obs
        return obs, reward, done, {}

    def render(self, mode="human"):
        if mode == "rgb_array":
            return self.last_obs

        elif mode == "human":
            from gym.utils import pyglet_rendering

            if self.viewer is None:
                self.viewer = pyglet_rendering.SimpleImageViewer()
            self.viewer.imshow(self.last_obs)
            return self.viewer.isopen

        else:
            assert 0, f"Render mode '{mode}' is not supported"

    def close(self):
        if self.viewer is not None:
            self.viewer.close()
            self.viewer = None


class CubeCrashSparse(CubeCrash):
    use_shaped_reward = False


class CubeCrashScreenBecomesBlack(CubeCrash):
    use_shaped_reward = False
    use_black_screen = True