In reinforcement learning problems, an agent has the task of learning a good or optimal strategy from interaction with his environment. At the start of the learning task, the agent...
Tom Croonenborghs, Kurt Driessens, Maurice Bruynoo...
A MAS architecture consisting of service centers is proposed. Within each service center, a mediator coordinates service delivery by allocating individual tasks to corresponding t...
This paper presents a novel method for on-line coordination in multiagent reinforcement learning systems. In this method a reinforcement-learning agent learns to select its action ...
Abstract. Approximate Policy Iteration (API) is a reinforcement learning paradigm that is able to solve high-dimensional, continuous control problems. We propose to exploit API for...
In this paper, we propose a serving system consisting intelligent agents processing society information in a multi-user domain. The agents use the similarity information on the us...