Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding

On large problems, reinforcement learning systems must use parame(cid:173) terized function approximators such as neural networks in order to gen(cid:173) eralize between similar situations and actions. In these cases there are no strong theoretical results on the accuracy of convergence, and com(cid:173) putational results have been mixed. In particular, Boyan and Moore reported at last year's meeting a series of negative results in attempting to apply dynamic programming together with function approximation to simple control problems with continuous state spaces. In this paper, we present positive results for all the control tasks they attempted, and for one that is significantly larger. The most important differences are that we used sparse-coarse-coded function approximators (CMACs) whereas they used mostly global function approximators, and that we learned online whereas they learned offline.