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RLCard: A Toolkit for Reinforcement Learning in Card Games
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
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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.
Monte Carlo Q-learning for General Game Playing
GGP problems can be solved by reinforcement learning.
Regular Boardgames
We propose a new General Game Playing (GGP) language called Regular Boardgames (RBG), which is based on the theory of regular languages.
A0C: Alpha Zero in Continuous Action Space
A core novelty of Alpha Zero is the interleaving of tree search and deep learning, which has proven very successful in board games like Chess, Shogi and Go.
Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning
Unlike other benchmarks such as the Arcade Learning Environment, evaluation of agent performance in Obstacle Tower is based on an agent's ability to perform well on unseen instances of the environment.
Assessing the Potential of Classical Q-learning in General Game Playing
For small games, simple classical table-based Q-learning might still be the algorithm of choice.