Potential-based shaping was designed as a way of introducing background knowledge into model-free reinforcement-learning algorithms. By identifying states that are likely to have ...
Abstract. In multi-agent reinforcement learning systems, it is important to share a reward among all agents. We focus on the Rationality Theorem of Profit Sharing [5] and analyze ...
When two or more agents interacting, their behaviors are not necessarily matching. Automated ways to overcome conicts in the behavior of agents can make the execution of interacti...
Temporal difference (TD) learning methods [22] have become popular reinforcement learning techniques in recent years. TD methods have had some experimental successes and have been...
We describe how to take a set of interaction traces produced by different pairs of players in a two-player repeated game, and combine them into a composite strategy. We provide an...