Natural gradient works efficiently in learning
Neural Computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement Learning in POMDPs with Function Approximation
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
SIAM Journal on Control and Optimization
Efficient reinforcement learning using recursive least-squares methods
Journal of Artificial Intelligence Research
Neurocomputing
Engineering Applications of Artificial Intelligence
Impedance learning for robotic contact tasks using natural actor-critic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Revisiting natural actor-critics with value function approximation
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
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Recently, actor-critic methods have drawn much interests in the area of reinforcement learning, and several algorithms have been studied along the line of the actor-critic strategy. This paper studies an actor-critic type algorithm utilizing the RLS(recursive least-squares) method, which is one of the most efficient techniques for adaptive signal processing, together with natural policy gradient. In the actor part of the studied algorithm, we follow the strategy of performing parameter update via the natural gradient method, while in its update for the critic part, the recursive least-squares method is employed in order to make the parameter estimation for the value functions more efficient. The studied algorithm was applied to locomotion of a two-linked robot arm, and showed better performance compared to the conventional stochastic gradient ascent algorithm.