Backpropagation: basics and new developments
The handbook of brain theory and neural networks
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Handbook of Learning and Approximate Dynamic Programming (IEEE Press Series on Computational Intelligence)
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
IEEE Transactions on Neural Networks
Online actor critic algorithm to solve the continuous-time infinite horizon optimal control problem
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Adaptive Critic Designs for Discrete-Time Zero-Sum Games With Application to Control
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Comparison of Adaptive Critic-Based and Classical Wide-Area Controllers for Power Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Issues on Stability of ADP Feedback Controllers for Dynamical Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Online learning control by association and reinforcement
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Adaptive Learning and Control for MIMO System Based on Adaptive Dynamic Programming
IEEE Transactions on Neural Networks
A novel adaptive tropism reward ADHDP method with robust property
BICS'13 Proceedings of the 6th international conference on Advances in Brain Inspired Cognitive Systems
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In this paper, we propose a novel adaptive dynamic programming (ADP) architecture with three networks, an action network, a critic network, and a reference network, to develop internal goal-representation for online learning and optimization. Unlike the traditional ADP design normally with an action network and a critic network, our approach integrates the third network, a reference network, into the actor-critic design framework to automatically and adaptively build an internal reinforcement signal to facilitate learning and optimization overtime to accomplish goals. We present the detailed design architecture and its associated learning algorithm to explain how effective learning and optimization can be achieved in this new ADP architecture. Furthermore, we test the performance of our architecture both on the cart-pole balancing task and the triple-link inverted pendulum balancing task, which are the popular benchmarks in the community to demonstrate its learning and control performance over time.