readme.md

Build an Othello/Reversi chess game environment with fancy GUI for Reinforcement Learning

Reversi (aka Othello) is a game that I knew and played since I was in middle school. You can play with a PC or another human player. The rule is very simple. You can have a try here. After trying many times I figured our myself without reading any tutorials that the positions at the four corners, and along the four boundaries are very critical places. You must think very carefully before putting your pieces there.

After so many years, I am now a researcher and a fan of AI. Suddenly one day I recalled this game and asked myself why not build an AI agent and train it to play Reversi. After some searches, I found that there are indeed many peoples sharing their experience of doing this. But their efforts are not on a fancy Reversi environment such as seemingly 3D pieces and board but on simply table-like texts (their algorithms / ML models are excellent though). I am not sure if there exist one for fun or research purposes, but I gave up further searching and decided to build a fancy Reversi environment for myself like the picture below:

As you can see, with this environment, you have the naturalness of the pieces and the board. You can see the hint of possible positions for the next player (the red "+" sign). You have piece counters. It will make the ML model developing process more interesting than boring text symbols.

In the following parts I will introduce how to build such a Reversi environment for your reinforcement learning algorithms. I build some RL agents with some very basic strategies. At least for now my agent is able to beat the random strategy at 80% probability.

All these are just for fun!

Element preparation

You need to prepare:

• a board of 8x8 empty grids (you can design it to be any fancy style you like)
• a white piece (or any other color you like)
• a black piece (or any other color you like)

You may notice that the pieces are not of a round shape, but a square with green background color identical to the board. It does not matter, when put on the board no one would tell it is the color of the board or the color from a piece.

That's enough for building the environment. Now we need some coding.

Python with Gym

My computer has dual operating systems (Windows + Ubuntu). All the following procedures are on Windows.

Platform information: Anaconda, PyCharm, opencv-python, gym. I assume you have no problem in setting this up.

reversi_env.py

It includes essential classes and methods for realizing the interactive Reversi game. The class Reversi may be the most important part:

class Reversi:
    def __init__(self, human_VS_machine=False):
        ...
    def reset(self):
        ...
    def __refresh_canvas(self):
        ...
    def render(self, mode='human'):
        ...
    def step(self, action: tuple):
        ...
    def __put(self, i:int, j:int, color:str):
        ...
    def __get_possible_actions(self, color):
        ...
    def get_random_action(self):
        ...
    def close(self):
        ...
    def __piece_count(self):
        ...
    def __check_termination(self):
        ...
    def get_human_action(self):
        ...

Actually, it just weakly relies on Gym since only the data type is used such as self.observation_space = spaces.Discrete(8 * 8), all the other realizations are indepandent from Gym (but the entire framework was referred to Gym realizations). Some ideas for realization and cautions are listed as below:

• Putting a piece in a grid is a process of: (1) getting the exact pixel coordinates in a grid, like (y:row, x:col), which represents the upper left position inside the grid where to put the piece). Remember, if you are designing your own board, you need to get the precise coordinates of the left upper pixels to be replaced by a piece. I did it using an image processing tool for convenience like below:

  

  (2) Replacing the pixels of part of the grid with the pixels of a piece. The size of a piece is a little smaller than the size of a grid.

• The global variable self.next_player indicates who is the next player. Normally each player takes its turn to put a piece on the board. Be aware of that in some cases when it is impossible for a player to put a piece, he loses his turn and the other player will continue to play.

• The method __get_possible_actions(self, color) is to get possible grids where the next player can put his piece. Remember that only the grids where he can reverse the opponent's color are allowed to put a piece. It returns a set of coordinates (each of which corresponds to a grid). The red "+" signs are shown based on the returned outputs of this method.

• The method get_human_action(self) reads the location of your mouse when clicked the left button and check if it is a legal grid to put a piece. If legal, a piece of a corresponding color will be put in the grid. Currently, if you click at an illegal grid for a certain number of times, a random legal grid will be chosen as the grid to put your peice.

• get_random_action(self) realizes a random strategy by choosing randomly a possible grid where a piece is going to be put.

• __put(self, i:int, j:int, color:str) implement the process of putting a piece and reversing your opponent's color to yours wherever the rule applicable.

• __refresh_canvas(self) and render(self, mode='human') refresh the visualized board with updated pieces, and display the updated counts of each color.

• __check_termination(self) checks whether the game is over. Normally the game is over when the board is full of pieces. Sometimes when it is impossible for either player to put a piece, the game ends in advance with a few empty grids.

That's probably it. All details about this environment are in the reversi_env.py file. Necessary comments are added. You can run the file reversi_env.py and begin to test it. By default, the env adopts a random strategy.

agent.py

This is some of my implementations of RL algorithms. Many algorithms are available, like DQN, Double DQN, PG, AC, DDPG, etc. This is where your ML knowledge can be applied. Some cautions I got from my implementations:

• The player puts the last piece on the board is not definitely the winner. So when a game is over, it might be wrong to assign rewards to the last player.
• Design proper rewards for each step as in my code those rewards are arbitrarily designed.
• You of course do not want to play with an agent for thousands of rounds for training it. It's better to have two agents and let one to learn and let the other one just to be updated at intervals.

train.py

The process of training your models. You are free to develop your own. My code might be a little messy in your eyes.

play_the_game.py

Play the game with a trained AI

Other

Hope you find this environment interesting and helpful. This repository might still be updated, but I am not sure when. Enjoy! ^_^