A neural field approach to topological reinforcement learning in continuous action spaces

We present a neural field approach to distributed Q-learning in continuous state and action spaces that is based on action coding and selection in dynamic neural fields. It is, to the best of our knowledge, one of the first attempts that combines the advantages of a topological action coding with a distributed action-value learning in one neural architecture. This combination, supplemented by a neural vector quantization technique for state space clustering, is the basis for a control architecture and learning scheme that meet the demands of reinforcement learning for real-world problems. The experimental results in learning a vision-based docking behavior, a hard delayed reinforcement learning problem, show that the learning process can be successfully accelerated and made robust by this kind of distributed reinforcement learning.