Reinforcement Learning Based on On-Line EM Algorithm

Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)

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Masa-aki Sato, Shin Ishii


In this article, we propose a new reinforcement learning (RL) method based on an actor-critic architecture. The actor and the critic are approximated by Normalized Gaussian Networks (NGnet), which are networks of local linear regression units. The NGnet is trained by the on-line EM algorithm proposed in our pre(cid:173) vious paper. We apply our RL method to the task of swinging-up and stabilizing a single pendulum and the task of balancing a dou(cid:173) ble pendulum near the upright position. The experimental results show that our RL method can be applied to optimal control prob(cid:173) lems having continuous state/action spaces and that the method achieves good control with a small number of trial-and-errors.