Board Games

42 papers with code • 0 benchmarks • 2 datasets

This task has no description! Would you like to contribute one?

Libraries

Use these libraries to find Board Games models and implementations

Most implemented papers

Solving Royal Game of Ur Using Reinforcement Learning

sidharth0094/game-of-ur-reinforcement-learning 23 Aug 2022

Reinforcement Learning has recently surfaced as a very powerful tool to solve complex problems in the domain of board games, wherein an agent is generally required to learn complex strategies and moves based on its own experiences and rewards received.

Move Evaluation in Go Using Deep Convolutional Neural Networks

jmgilmer/GoCNN 20 Dec 2014

The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function.

Beating the World's Best at Super Smash Bros. with Deep Reinforcement Learning

shaneallcroft/SSB64RLBot 21 Feb 2017

There has been a recent explosion in the capabilities of game-playing artificial intelligence.

The Text-Based Adventure AI Competition

Microsoft/nail_agent 3 Aug 2018

In 2016, 2017, and 2018 at the IEEE Conference on Computational Intelligence in Games, the authors of this paper ran a competition for agents that can play classic text-based adventure games.

Assessing the Potential of Classical Q-learning in General Game Playing

wh1992v/ggp-rl 14 Oct 2018

For small games, simple classical table-based Q-learning might still be the algorithm of choice.

Biasing MCTS with Features for General Games

Ludeme/LudiiAI 21 Mar 2019

This paper proposes using a linear function approximator, rather than a deep neural network (DNN), to bias a Monte Carlo tree search (MCTS) player for general games.

State Representation and Polyomino Placement for the Game Patchwork

zayenz/cp-mod-ref-2019-patchwork 13 Jan 2020

Modern board games are a rich source of entertainment for many people, but also contain interesting and challenging structures for game playing research and implementing game playing agents.

Nmbr9 as a Constraint Programming Challenge

zayenz/cp-2019-nmbr9 13 Jan 2020

Modern board games are a rich source of interesting and new challenges for combinatorial problems.

Explain Your Move: Understanding Agent Actions Using Focused Feature Saliency

rl-interpretation/understandingRL ICLR 2020

We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that our approach generates saliency maps that are more interpretable for humans than existing approaches.

Manipulating the Distributions of Experience used for Self-Play Learning in Expert Iteration

Ludeme/LudiiAI 30 May 2020

ExIt involves training a policy to mimic the search behaviour of a tree search algorithm - such as Monte-Carlo tree search - and using the trained policy to guide it.