This paper introduces a multiagent reinforcement learning algorithm that converges with a given accuracy to stationary Nash equilibria in general-sum discounted stochastic games. ...
The agents in multiagent systems can coordinate their actions and handle tasks jointly by forming coalitions. One of the important steps in this process is the fair division of pa...
This paper is concerned with how multi-agent reinforcement learning algorithms can practically be applied to real-life problems. Recently, a new coordinated multi-agent exploratio...
Decision-theoretic models have become increasingly popular as a basis for solving agent and multiagent problems, due to their ability to quantify the complex uncertainty and prefe...
Being able to ensure that a multiagent system will not generate undesirable behaviors is essential within the context of critical applications (embedded systems or real-time system...
Caroline Chopinaud, Amal El Fallah-Seghrouchni, Pa...