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...
Single-agent reinforcement learners in time-extended domains and multi-agent systems share a common dilemma known as the credit assignment problem. Multi-agent systems have the st...
In this paper I give a brief overview of recent work on uncertainty inAI, and relate it to logical representations. Bayesian decision theory and logic are both normative frameworks...
This paper presents a scalable and self-optimizing architecture for Quality-of-Service (QoS) provisioning in the Differentiated Services (DiffServ) framework. The proposed archite...
This paper describes a computationally feasible approximation to the AIXI agent, a universal reinforcement learning agent for arbitrary environments. AIXI is scaled down in two ke...
Joel Veness, Kee Siong Ng, Marcus Hutter, William ...