Integral reinforcement learning and experience replay for adaptive optimal control of partially-unknown constrained-input continuous-time systems

نویسنده:
, , , ,
سال
: 2014
چکیده: In this paper, an integral reinforcement learning (IRL) algorithm on an actor–critic structure is developed

to learn online the solution to the Hamilton–Jacobi–Bellman equation for partially-unknown constrainedinput

systems. The technique of experience replay is used to update the critic weights to solve an

IRL Bellman equation. This means, unlike existing reinforcement learning algorithms, recorded past

experiences are used concurrently with current data for adaptation of the critic weights. It is shown that

using this technique, instead of the traditional persistence of excitation condition which is often difficult

or impossible to verify online, an easy-to-check condition on the richness of the recorded data is sufficient

to guarantee convergence to a near-optimal control law. Stability of the proposed feedback control law is

shown and the effectiveness of the proposed method is illustrated with simulation examples
کلیدواژه(گان): Integral reinforcement learning

Experience replay

Optimal control

Neural networks

Input constraints
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