4 papers
Towards the Automatic Synthesis of Interpretable Chess Tactics
Abhijeet Krishnan, Chris Martens
State-of-the-art reinforcement learning agents are capable of outperforming human experts at games like chess, Go and StarCraft II. These agents do not simply take advantage of the…
Learning Explainable Representations of Complex Game-playing Strategies
Abhijeet Krishnan, Colin M. Potts, Arnav Jhala +3
As part of learning to play complex games, human players develop develop abstractions for concepts and strategies of gameplay consistent with game rules to improve their performanc…
Modeling Risk in Reinforcement Learning: A Literature Mapping
Leonardo Villalobos-Arias, Derek Martin, Abhijeet Krishnan +3
Safe reinforcement learning deals with mitigating or avoiding unsafe situations by reinforcement learning (RL) agents. Safe RL approaches are based on specific risk representations…
Towards Action Model Learning for Player Modeling
Abhijeet Krishnan, Aaron Williams, Chris Martens
Player modeling attempts to create a computational model which accurately approximates a player's behavior in a game. Most player modeling techniques rely on domain knowledge and a…