Board Games
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Greatest papers with code
RLCard: A Toolkit for Reinforcement Learning in Card Games
• datamllab/rlcard
• 
The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with multiple agents, large state and action space, and sparse reward.
BOARD GAMES GAME OF POKER MULTI-AGENT REINFORCEMENT LEARNING
Move Evaluation in Go Using Deep Convolutional Neural Networks
• jmgilmer/GoCNN
• 
The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function.
Learning to play the Chess Variant Crazyhouse above World Champion Level with Deep Neural Networks and Human Data
Crazyhouse is a game with a higher branching factor than chess and there is only limited data of lower quality available compared to AlphaGo.
The Text-Based Adventure AI Competition
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.
Learning to Shoot in First Person Shooter Games by Stabilizing Actions and Clustering Rewards for Reinforcement Learning
While reinforcement learning (RL) has been applied to turn-based board games for many years, more complex games involving decision-making in real-time are beginning to receive more attention.
Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution
We show through illustrative examples (Chess, Atari, Go), human studies (Chess), and automated evaluation methods (Chess) that SARFA generates saliency maps that are more interpretable for humans than existing approaches.
Regular Boardgames
We propose a new General Game Playing (GGP) language called Regular Boardgames (RBG), which is based on the theory of regular languages.
Explain Your Move: Understanding Agent Actions Using Focused Feature Saliency
ICLR 2020
• rl-interpretation/understandingRL
•
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.
Monte Carlo Q-learning for General Game Playing
GGP problems can be solved by reinforcement learning.
Nmbr9 as a Constraint Programming Challenge
Modern board games are a rich source of interesting and new challenges for combinatorial problems.