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Efficient Model Learning Methods for Actor–Critic Control

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5 Author(s)
Grondman, I. ; Delft Center for Syst. & Control, Delft Univ. of Technol., Delft, Netherlands ; Vaandrager, M. ; Busoniu, L. ; Babuska, R.
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We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.

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Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:42 ,  Issue: 3 )