论文信息 - A Neuro-fuzzy Learning System for Adaptive Swarm Behaviors Dealing with Continuous State Space

A Neuro-fuzzy Learning System for Adaptive Swarm Behaviors Dealing with Continuous State Space

Swarm intelligence has brought a new paradise for function optimization, structural optimization, multi-agent systems and other study fields. In our previous work, we proposed a neuro-fuzzy system using reinforcement learning algorithm (actor-critic method with TD error learning algorithm) to acquire optimized swarm behaviors. This paper improves the conventional learning system, which only deals with discrete state space and action space, to solve how a swarm to learn and obtain its adaptive behaviors in the continuous state space. The improved system adopts a new policy function of action which is possible to yield continuous actions corresponding to continuous states. The effectiveness of proposed system is investigated by computer simulations with more kinds of environments for the goal-exploration problem.

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