We present an anytime multiagent learning approach to satisfy any given optimality criterion in repeated game self-play. Our approach is opposed to classical learning approaches fo...
In this paper we propose a multiagent architecture for implementing concurrent reinforcement learning, an approach where several agents, sharing the same environment, perceptions ...
A typical goal for transfer learning algorithms is to utilize knowledge gained in a source task to learn a target task faster. Recently introduced transfer methods in reinforcemen...
In this paper, we present an improved version of the online phase-space learning algorithm of Tsung and Cottrell (1995), called ARTISTE (Autonomous Real-TIme Selection of Training...
Learning the reward function of an agent by observing its behavior is termed inverse reinforcement learning and has applications in learning from demonstration or apprenticeship l...