From d995b315e1670a65c970a2c55932641ba5a0e477 Mon Sep 17 00:00:00 2001 From: Nicolai Czempin Date: Mon, 3 Apr 2017 21:55:40 +0200 Subject: [PATCH] Update README.md --- README.md | 14 ++++---------- 1 file changed, 4 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 3593be2..a026070 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,7 @@ -# gym-random-walk +# gym-tic-tac-toe -A minimal example of a custom environment for https://github.com/openai/gym. +An example of a custom environment for https://github.com/openai/gym. -What the environment provides is not that important; this is meant to show how what you need to do to create your own environments for openai/gym. +I want to try out self-play in a Reinforcement Learning context. Rather than the board game environments on openai/gym right now, which are "single-player" by providing a built-in opponent, I want to create an agent that learns a strategy by playing against itself, so it will try to maximize the reward for "player 1" and minimize it for "player 2". -For concreteness I used an example in the recordings of David Silver's lectures on Reinforcement Learning at UCL. - -(0) - A - B - C - D - E - (+1) - -You start off at one of the positions A to E, you can move right or left, reaching the "+1" terminal state gives you a reward of +1, and going all the way "to the left" will give you a terminal reward of 0. - -Instead of calling them the above, I just made them the states 0, 1, ...6. +The canonical example of a simple two player game is Tic Tac Toe, also known as Noughts and Crosses.