Gym-MiniGrid/gym_minigrid/wrappers.py

import math
import operator
from functools import reduce

import numpy as np
import gym
from gym import error, spaces, utils
from .minigrid import OBJECT_TO_IDX, COLOR_TO_IDX, STATE_TO_IDX, Goal

class ReseedWrapper(gym.core.Wrapper):
    """
    Wrapper to always regenerate an environment with the same set of seeds.
    This can be used to force an environment to always keep the same
    configuration when reset.
    """

    def __init__(self, env, seeds=[0], seed_idx=0):
        self.seeds = list(seeds)
        self.seed_idx = seed_idx
        super().__init__(env)

    def reset(self, **kwargs):
        seed = self.seeds[self.seed_idx]
        self.seed_idx = (self.seed_idx + 1) % len(self.seeds)
        self.env.seed(seed)
        return self.env.reset(**kwargs)

    def step(self, action):
        obs, reward, done, info = self.env.step(action)
        return obs, reward, done, info

class ActionBonus(gym.core.Wrapper):
    """
    Wrapper which adds an exploration bonus.
    This is a reward to encourage exploration of less
    visited (state,action) pairs.
    """

    def __init__(self, env):
        super().__init__(env)
        self.counts = {}

    def step(self, action):
        obs, reward, done, info = self.env.step(action)

        env = self.unwrapped
        tup = (tuple(env.agent_pos), env.agent_dir, action)

        # Get the count for this (s,a) pair
        pre_count = 0
        if tup in self.counts:
            pre_count = self.counts[tup]

        # Update the count for this (s,a) pair
        new_count = pre_count + 1
        self.counts[tup] = new_count

        bonus = 1 / math.sqrt(new_count)
        reward += bonus

        return obs, reward, done, info

    def reset(self, **kwargs):
        return self.env.reset(**kwargs)

class StateBonus(gym.core.Wrapper):
    """
    Adds an exploration bonus based on which positions
    are visited on the grid.
    """

    def __init__(self, env):
        super().__init__(env)
        self.counts = {}

    def step(self, action):
        obs, reward, done, info = self.env.step(action)

        # Tuple based on which we index the counts
        # We use the position after an update
        env = self.unwrapped
        tup = (tuple(env.agent_pos))

        # Get the count for this key
        pre_count = 0
        if tup in self.counts:
            pre_count = self.counts[tup]

        # Update the count for this key
        new_count = pre_count + 1
        self.counts[tup] = new_count

        bonus = 1 / math.sqrt(new_count)
        reward += bonus

        return obs, reward, done, info

    def reset(self, **kwargs):
        return self.env.reset(**kwargs)

class ImgObsWrapper(gym.core.ObservationWrapper):
    """
    Use the image as the only observation output, no language/mission.
    """

    def __init__(self, env):
        super().__init__(env)
        self.observation_space = env.observation_space.spaces['image']

    def observation(self, obs):
        return obs['image']

class OneHotPartialObsWrapper(gym.core.ObservationWrapper):
    """
    Wrapper to get a one-hot encoding of a partially observable
    agent view as observation.
    """

    def __init__(self, env, tile_size=8):
        super().__init__(env)

        self.tile_size = tile_size

        obs_shape = env.observation_space['image'].shape

        # Number of bits per cell
        num_bits = len(OBJECT_TO_IDX) + len(COLOR_TO_IDX) + len(STATE_TO_IDX)

        self.observation_space.spaces["image"] = spaces.Box(
            low=0,
            high=255,
            shape=(obs_shape[0], obs_shape[1], num_bits),
            dtype='uint8'
        )

    def observation(self, obs):
        img = obs['image']
        out = np.zeros(self.observation_space.spaces['image'].shape, dtype='uint8')

        for i in range(img.shape[0]):
            for j in range(img.shape[1]):
                type = img[i, j, 0]
                color = img[i, j, 1]
                state = img[i, j, 2]

                out[i, j, type] = 1
                out[i, j, len(OBJECT_TO_IDX) + color] = 1
                out[i, j, len(OBJECT_TO_IDX) + len(COLOR_TO_IDX) + state] = 1

        return {
            **obs,
            'image': out
        }

class RGBImgObsWrapper(gym.core.ObservationWrapper):
    """
    Wrapper to use fully observable RGB image as observation,
    This can be used to have the agent to solve the gridworld in pixel space.
    """

    def __init__(self, env, tile_size=8):
        super().__init__(env)

        self.tile_size = tile_size

        self.observation_space.spaces['image'] = spaces.Box(
            low=0,
            high=255,
            shape=(self.env.width * tile_size, self.env.height * tile_size, 3),
            dtype='uint8'
        )

    def observation(self, obs):
        env = self.unwrapped

        rgb_img = env.render(
            mode='rgb_array',
            highlight=False,
            tile_size=self.tile_size
        )

        return {
            **obs,
            'image': rgb_img
        }


class RGBImgPartialObsWrapper(gym.core.ObservationWrapper):
    """
    Wrapper to use partially observable RGB image as observation.
    This can be used to have the agent to solve the gridworld in pixel space.
    """

    def __init__(self, env, tile_size=8):
        super().__init__(env)

        self.tile_size = tile_size

        obs_shape = env.observation_space.spaces['image'].shape
        self.observation_space.spaces['image'] = spaces.Box(
            low=0,
            high=255,
            shape=(obs_shape[0] * tile_size, obs_shape[1] * tile_size, 3),
            dtype='uint8'
        )

    def observation(self, obs):
        env = self.unwrapped

        rgb_img_partial = env.get_obs_render(
            obs['image'],
            tile_size=self.tile_size
        )

        return {
            **obs,
            'image': rgb_img_partial
        }

class FullyObsWrapper(gym.core.ObservationWrapper):
    """
    Fully observable gridworld using a compact grid encoding
    """

    def __init__(self, env):
        super().__init__(env)

        self.observation_space.spaces["image"] = spaces.Box(
            low=0,
            high=255,
            shape=(self.env.width, self.env.height, 3),  # number of cells
            dtype='uint8'
        )

    def observation(self, obs):
        env = self.unwrapped
        full_grid = env.grid.encode()
        full_grid[env.agent_pos[0]][env.agent_pos[1]] = np.array([
            OBJECT_TO_IDX['agent'],
            COLOR_TO_IDX['red'],
            env.agent_dir
        ])

        return {
            **obs,
            'image': full_grid
        }

class DictObservationSpaceWrapper(gym.core.ObservationWrapper):
    """
    Use a Dict Obsevation Space encoding images, missions, and directions
    """

    def __init__(self, env, max_words_in_mission=50, word_dict=None):
        """
        max_words_in_mission is the length of the array to represent a mission, value 0 for missing words
        word_dict is a dictionary of words to use (keys=words, values=indices from 1 to < max_words_in_mission),
                  if None, use the Minigrid language
        """
        super().__init__(env)

        if word_dict is None:
            word_dict = DictObservationSpaceWrapper.get_minigrid_words()
            
        self.max_words_in_mission = max_words_in_mission
        self.word_dict = word_dict

        image_observation_space = spaces.Box(
            low=0,
            high=255,
            shape=(self.agent_view_size, self.agent_view_size, 3),
            dtype='uint8'
        )
        self.observation_space = spaces.Dict({
            'image': image_observation_space,
            'direction': spaces.Discrete(4),
            'mission': spaces.MultiDiscrete([len(self.word_dict.keys())]
             * max_words_in_mission)
        })

    @staticmethod
    def get_minigrid_words():
        colors = ['red', 'green', 'blue', 'yellow', 'purple', 'grey']
        objects = ['unseen', 'empty', 'wall', 'floor', 'box', 'key', 'ball',
        'door', 'goal', 'agent', 'lava']

        verbs = ['pick', 'avoid', 'get', 'find', 'put',
                'use', 'open', 'go', 'fetch',
                'reach', 'unlock', 'traverse']

        extra_words = ['up', 'the', 'a', 'at', ',', 'square',
                    'and', 'then', 'to', 'of', 'rooms', 'near',
                    'opening', 'must', 'you', 'matching', 'end',
                    'hallway', 'object', 'from', 'room']


        all_words = colors + objects + verbs + extra_words
        assert len(all_words) == len(set(all_words))
        return {word: i for i, word in enumerate(all_words)}

    def string_to_indices(self, string, offset=1):
        """
        Convert a string to a list of indices.
        """
        indices = []
        string = string.replace(',', ' , ')  # adding space before and after commas
        for word in string.split():
            if word in self.word_dict.keys():
                indices.append(self.word_dict[word] + offset)
            else:
                raise ValueError('Unknown word: {}'.format(word))
        return indices
        
    def observation(self, obs):
        obs['mission'] = self.string_to_indices(obs['mission'])
        assert len(obs['mission']) < self.max_words_in_mission
        obs['mission'] += [0] * (self.max_words_in_mission - len(obs['mission']))

        return obs
        
class FlatObsWrapper(gym.core.ObservationWrapper):
    """
    Encode mission strings using a one-hot scheme,
    and combine these with observed images into one flat array
    """

    def __init__(self, env, maxStrLen=96):
        super().__init__(env)

        self.maxStrLen = maxStrLen
        self.numCharCodes = 27

        imgSpace = env.observation_space.spaces['image']
        imgSize = reduce(operator.mul, imgSpace.shape, 1)

        self.observation_space = spaces.Box(
            low=0,
            high=255,
            shape=(imgSize + self.numCharCodes * self.maxStrLen,),
            dtype='uint8'
        )

        self.cachedStr = None
        self.cachedArray = None

    def observation(self, obs):
        image = obs['image']
        mission = obs['mission']

        # Cache the last-encoded mission string
        if mission != self.cachedStr:
            assert len(mission) <= self.maxStrLen, 'mission string too long ({} chars)'.format(len(mission))
            mission = mission.lower()

            strArray = np.zeros(shape=(self.maxStrLen, self.numCharCodes), dtype='float32')

            for idx, ch in enumerate(mission):
                if ch >= 'a' and ch <= 'z':
                    chNo = ord(ch) - ord('a')
                elif ch == ' ':
                    chNo = ord('z') - ord('a') + 1
                assert chNo < self.numCharCodes, '%s : %d' % (ch, chNo)
                strArray[idx, chNo] = 1

            self.cachedStr = mission
            self.cachedArray = strArray

        obs = np.concatenate((image.flatten(), self.cachedArray.flatten()))

        return obs

class ViewSizeWrapper(gym.core.Wrapper):
    """
    Wrapper to customize the agent field of view size.
    This cannot be used with fully observable wrappers.
    """

    def __init__(self, env, agent_view_size=7):
        super().__init__(env)

        assert agent_view_size % 2 == 1
        assert agent_view_size >= 3

        # Override default view size
        env.unwrapped.agent_view_size = agent_view_size

        # Compute observation space with specified view size
        observation_space = gym.spaces.Box(
            low=0,
            high=255,
            shape=(agent_view_size, agent_view_size, 3),
            dtype='uint8'
        )

        # Override the environment's observation space
        self.observation_space = spaces.Dict({
            'image': observation_space
        })

    def reset(self, **kwargs):
        return self.env.reset(**kwargs)

    def step(self, action):
        return self.env.step(action)

class DirectionObsWrapper(gym.core.ObservationWrapper):
    """
    Provides the slope/angular direction to the goal with the observations as modeled by (y2 - y2 )/( x2 - x1)
    type = {slope , angle}
    """
    def __init__(self, env,type='slope'):
        super().__init__(env)
        self.goal_position = None
        self.type = type

    def reset(self):
        obs = self.env.reset()
        if not self.goal_position:
            self.goal_position = [x for x,y in enumerate(self.grid.grid) if isinstance(y,(Goal) ) ]
            if len(self.goal_position) >= 1: # in case there are multiple goals , needs to be handled for other env types
                self.goal_position = (int(self.goal_position[0]/self.height) , self.goal_position[0]%self.width)
        return obs

    def observation(self, obs):
        slope = np.divide( self.goal_position[1] - self.agent_pos[1] ,  self.goal_position[0] - self.agent_pos[0])
        obs['goal_direction'] = np.arctan( slope ) if self.type == 'angle' else slope
        return obs

class SymbolicObsWrapper(gym.core.ObservationWrapper):
    """
    Fully observable grid with a symbolic state representation.
    The symbol is a triple of (X, Y, IDX), where X and Y are
    the coordinates on the grid, and IDX is the id of the object.
    """

    def __init__(self, env):
        super().__init__(env)

        self.observation_space.spaces["image"] = spaces.Box(
            low=0,
            high=max(OBJECT_TO_IDX.values()),
            shape=(self.env.width, self.env.height, 3),  # number of cells
            dtype="uint8",
        )

    def observation(self, obs):
        objects = np.array(
            [OBJECT_TO_IDX[o.type] if o is not None else -1 for o in self.grid.grid]
        )
        w, h = self.width, self.height
        grid = np.mgrid[:w, :h]
        grid = np.concatenate([grid, objects.reshape(1, w, h)])
        grid = np.transpose(grid, (1, 2, 0))
        obs['image'] = grid
        return obs