Online support vector regression based actor-critic method

This paper proposes a new algorithm for actor-critic method using online support vector regression(SVR), which can do incremental learning and automatically track variation of environment with time-varying characteristics. It gives good generalization properties to value function approximation and helps the critic converge fast. In addition, sample vectors in data set of the online SVR are used as center positions of actor's basis functions. Actor updates policy parameters with those functions using policy gradient algorithm. Throughout simulations, the feasibility and usefulness of the proposed method is demonstrated by comparison with other methods.