We introduce a new class of Reinforcement Learning algorithms designed to operate in perceptual spaces containing images. They work by classifying the percepts using a computer vi...
An original Reinforcement Learning (RL) methodology is proposed for the design of multi-agent systems. In the realistic setting of situated agents with local perception, the task o...
In this paper, we focus on the coordination issues in a multiagent setting. Two coordination algorithms based on reinforcement learning are presented and theoretically analyzed. O...
Abstract. This paper explores the capabilities of continuous time recurrent neural networks (CTRNNs) to display reinforcement learning-like abilities on a set of T-Maze and double ...
In this paper the notion of a partial-order plan is extended to task-hierarchies. We introduce the concept of a partial-order taskhierarchy that decomposes a problem using multi-ta...