Blocking Maze for OpenAI Gym

blocking_maze_env01.py

# OpenAI gym custom environment mimicking Blocking Maze
# See Sutton and Barto "Reinforcement Learning an Introduction"
#     Example 8.2: Blocking Maze
from enum import Enum
import sys
import copy

import gym
from gym import error, spaces, utils
from gym.utils import seeding

MAP1 = [
    "+---------+",
    "|        G|",
    "|         |",
    "|         |",
    "|wwwwwwww |",
    "|         |",
    "|   s     |",
    "+---------+",
]

MAP2 = [
    "+---------+",
    "|        G|",
    "|         |",
    "|         |",
    "| wwwwwwww|",
    "|         |",
    "|   s     |",
    "+---------+",
]

class Action(Enum):
    UP = 0
    DOWN = 1
    LEFT = 2
    RIGHT = 3

class MazeEnv1(gym.Env):
    metadata = {'render.modes': ['human']}

    def __init__(self):
        print("init")
        self.s_loc = (6, 4)
        self.a_loc = (6, 4)
        self.g_loc = (1, 9)
        self.invalid = ['w', '-', '|']
        
        self.action_space = spaces.Discrete(len(self.actions))
        self.observation_space = spaces.Discrete(46) # temporary hard coded

        if not hasattr(self, 'map'):
            self.map = MAP1

    def step(self, action):
        current_loc = self.a_loc
        if action == Action.UP.value:
            self.a_loc = (self.a_loc[0]+1, self.a_loc[1])
        elif action == Action.DOWN.value:
            self.a_loc = (self.a_loc[0]-1, self.a_loc[1])
        elif action == Action.LEFT.value:
            self.a_loc = (self.a_loc[0], self.a_loc[1]-1)
        elif action == Action.RIGHT.value:
            self.a_loc = (self.a_loc[0], self.a_loc[1]+1)
        else:
            print("Invalid action is specified")
            raise ValueError

        reward = 0
        done = False
        info = 

        if self.map[self.a_loc[0]][self.a_loc[1]] in self.invalid:
            # if the move is invalid, return it back
            self.a_loc = current_loc

        if self.map[self.a_loc[0]][self.a_loc[1]] == 'G':
            reward = 1
            done = True

        return self.a_loc, reward, done, info
 
    def reset(self):
        print("reset")
        self.__init__()
        return self.tuple2int(self.a_loc)

    def render(self, mode='human'):
        outfile = sys.stdout
        m = copy.deepcopy(self.map)
        row = m[self.a_loc[0]]
        row = row[:self.a_loc[1]]+utils.colorize('A', 'red', bold=True)+row[self.a_loc[1]+1:]
        m[self.a_loc[0]] = row
        outfile.write("\n".join(["".join(row) for row in m])+"\n")
        outfile.write("\n")

    def close(self):
        print("close")

    def switch_maze(self):
        self.map = MAP2
        self.reset()

    @property
    def row_length(self):
        return len(self.map)-2

    @property
    def column_length(self):
        return len(self.gmap[0])-2

    @property
    def actions(self):
        return [Action.UP, Action.DOWN,
                Action.LEFT, Action.RIGHT]