This paper presents a novel framework for simultaneously learning representation and control in continuous Markov decision processes. Our approach builds on the framework of proto...
In multiagent environments, forms of social learning such as teaching and imitation have been shown to aid the transfer of knowledge from experts to learners in reinforcement lear...
Agent-based computing in Arti cial Intelligence has given rise to a number of diverse and competing proposals for agent programming languages. Agents, in the sense we are using it...
Koen V. Hindriks, Frank S. de Boer, Wiebe van der ...
In this paper, we investigate Reinforcement learning (RL) in multi-agent systems (MAS) from an evolutionary dynamical perspective. Typical for a MAS is that the environment is not ...
Karl Tuyls, Pieter Jan't Hoen, Bram Vanschoenwinke...
We consider multiagent systems situated in unpredictable environments. Agents viewed as abductive logic programs with abducibles being literals the agent could sense or receive fro...