Kernel-Based Reinforcement Learning

We consider the problem of approximating the cost-to-go functions in reinforcement learning. By mapping the state implicitly into a feature space, we perform a simple algorithm in the feature space, which corresponds to a complex algorithm in the original state space. Two kernel-based reinforcement learning algorithms, the e-insensitive kernel based reinforcement learning (epsilon-KRL) and the least squares kernel based reinforcement learning (LS-KRL) are proposed. An example shows that the proposed methods can deal effectively with the reinforcement learning problem without having to explore many states.