HEX and Neurodynamic Programming

by   Debangshu Banerjee, et al.

Hex is a complex game with a high branching factor. For the first time Hex is being attempted to be solved without the use of game tree structures and associated methods of pruning. We also are abstaining from any heuristic information about Virtual Connections or Semi Virtual Connections which were previously used in all previous known computer versions of the game. The H-search algorithm which was the basis of finding such connections and had been used with success in previous Hex playing agents has been forgone. Instead what we use is reinforcement learning through self play and approximations through neural networks to by pass the problem of high branching factor and maintaining large tables for state-action evaluations. Our code is based primarily on NeuroHex. The inspiration is drawn from the recent success of AlphaGo Zero.



page 1

page 2

page 3

page 4


Application of Self-Play Reinforcement Learning to a Four-Player Game of Imperfect Information

We introduce a new virtual environment for simulating a card game known ...

Learning to Play Two-Player Perfect-Information Games without Knowledge

In this paper, several techniques for learning game state evaluation fun...

Dynamic Move Chains -- a Forward Pruning Approach to Tree Search in Computer Chess

This paper proposes a new mechanism for pruning a search game-tree in co...

KnightCap: A chess program that learns by combining TD(lambda) with game-tree search

In this paper we present TDLeaf(lambda), a variation on the TD(lambda) a...

Playing 2048 With Reinforcement Learning

The game of 2048 is a highly addictive game. It is easy to learn the gam...

Applying supervised and reinforcement learning methods to create neural-network-based agents for playing StarCraft II

Recently, multiple approaches for creating agents for playing various co...

A New Challenge: Approaching Tetris Link with AI

Decades of research have been invested in making computer programs for p...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.