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Can I read Q-Learning for Feedback Nash Strategy of Finite-Horizon Nonzero-Sum Difference Games on EtoBox?
Q-Learning for Feedback Nash Strategy of Finite-Horizon Nonzero-Sum Difference Games by Zhaorong Zhang; Juanjuan Xu; Minyue Fu is a Computer Science article available to read on EtoBox.
What is Q-Learning for Feedback Nash Strategy of Finite-Horizon Nonzero-Sum Difference Games about?
In this article, we study the feedback Nash strategy of the model-free nonzero-sum difference game. The main contribution is to present the Q-learning algorithm for the linear quadratic game without prior knowledge of the system model. It is noted that the studied game is in finite horizon which is novel to the learning algorithms in the literature which are mostly for the infinite-horizon Nash strategy. The key is to characterize the Q-factors in terms of the arbitrary control input and state information. A numerical example is given to verify the effectiveness of the proposed algorithm.
Who reads Q-Learning for Feedback Nash Strategy of Finite-Horizon Nonzero-Sum Difference Games?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Zhaorong Zhang; Juanjuan Xu; Minyue Fu
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Published
- 2022
- Language
- EN
- Field
- Computer Science (Physical Sciences)