<commit_before># Playing Othello(Reversi) By Reinforcement Learning #

## Introduction ##
This is simple application that learn playing Othello by
*reinforcement learning*.

TD(0) is used to evaluate a policy.

Value approximation function is based on *n-tuple network* introduced
in Wojciech's paper.


## Reference ##
- <NAME> (2014). Systematic n-tuple networks for
  othello position evaluation. ICGA Journal, 37(2), 85–96.

- <NAME>., & <NAME>. (1998). Reinforcement learning: an
  introduction. : MIT press Cambridge.<commit_msg>add quick start in readme<commit_after># Playing Othello(Reversi) By Reinforcement Learning #

## Introduction ##
This is simple application that learn playing Othello by
*reinforcement learning*.

TD(0) is used to evaluate a policy.

Value approximation function is based on *n-tuple network* introduced
in Wojciech's paper.

## Quick Start ##

Run `python tdl.py` to learn a policy by self-play.

Edit `config/config.ini` to setup players and run `python run.py` to
play Othello in command line.


## Reference ##
- <NAME> (2014). Systematic n-tuple networks for
  othello position evaluation. ICGA Journal, 37(2), 85–96.

- <NAME>., & <NAME>. (1998). Reinforcement learning: an
  introduction. : MIT press Cambridge.
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