Learning to Weigh Basic Behaviors in Scalable gents

We are working on the use of Reinforcement Learning (RL)[3] algorithms to design automatically reactive situated agents limited to only local perceptions. Unfortunately, as good RL algorithms suffer from combinatorial explosion, their use is generally limited to simple problems. As shown on the tile-world example of figure 1, we propose to overcome these difficulties by making the hypothesis, as in Brook’s subsumption architecture [1], that a complex problem can be efficiently dealt with if considered as a combination of simple problems.