Recent advances in technology allow multi-agent systems to be deployed in cooperation with or as a service for humans. Typically, those systems are designed assuming individually ...
Reward shaping is a well-known technique applied to help reinforcement-learning agents converge more quickly to nearoptimal behavior. In this paper, we introduce social reward sha...
Monica Babes, Enrique Munoz de Cote, Michael L. Li...
In this study, we examine human social interactions within virtual worlds and address the question of how group interactions are affected by the surrounding game environment. To i...
Although many powerful AI and machine learning techniques exist, it remains difficult to quickly create AI for embodied virtual agents that produces visually lifelike behavior. T...
Jonathan Dinerstein, Parris K. Egbert, Dan Ventura
We introduce an approach to autonomously creating state space abstractions for an online reinforcement learning agent using a relational representation. Our approach uses a tree-b...