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...
The ability to determine a sequence of actions in order to reach a particular goal is of utmost importance to mobile robots. One major problem with symbolic planning approaches re...
In concurrent cooperative multiagent learning, each agent simultaneously learns to improve the overall performance of the team, with no direct control over the actions chosen by i...
Abstract. This paper documents the development of three autonomous stocktrading agents within the framework of the Penn Exchange Simulator (PXS), a novel stock-trading simulator th...
Commitment protocols formalize interactions among autonomous, heterogeneous agents, leaving the agents’ local policies unspecified. This paper studies the problem of agents ena...