Neural Network Control of Robot Manipulators and Nonlinear Systems
Neural Network Control of Robot Manipulators and Nonlinear Systems
Reinforcement Learning in Continuous Time and Space
Neural Computation
Batch reinforcement learning in a complex domain
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Brief paper: Adaptive optimal control for continuous-time linear systems based on policy iteration
Automatica (Journal of IFAC)
Reinforcement learning and adaptive dynamic programming for feedback control
IEEE Circuits and Systems Magazine
Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem
Automatica (Journal of IFAC)
Concurrent learning for convergence in adaptive control without persistency of excitation
Concurrent learning for convergence in adaptive control without persistency of excitation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Automatica (Journal of IFAC)
Experience Replay for Real-Time Reinforcement Learning Control
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Automatica (Journal of IFAC)
Hi-index | 22.14 |
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 constrained-input 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.