共被期刊论文引用了3次
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| 1 | Policy iteration based Q-learning for linear nonzero-sum quadratic differential games显示文摘In this paper, a policy iteration-based Q-learning algorithm is proposed to solve infinite horizon linear nonzero-sum quadratic differential games with completely unknown dynamics. The Q-learning algorithm, which employs off-policy reinforcement learning(RL), can learn the Nash equilibrium and the corresponding value functions online, using the data sets generated by behavior policies. First, we prove equivalence between the proposed off-policy Q-learning algorithm and an offline PI algorithm by selecting specific initially admissible polices that can be learned online. Then, the convergence of the off-policy Qlearning algorithm is proved under a mild rank condition that can be easily met by injecting appropriate probing noises into behavior policies. The generated data sets can be repeatedly used during the learning process, which is computationally effective. The simulation results demonstrate the effectiveness of the proposed Q-learning algorithm. | Xinxing LI Zhihong PENG Li LIANG Wenzhong ZHA | 2019 | Science China(Information Sciences)2019，62，5： | 6 |
| 2 | Online adaptive Q-learning method for fully cooperative linear quadratic dynamic games显示文摘A model-based offline policy iteration(PI) algorithm and a model-free online Q-learning algorithm are proposed for solving fully cooperative linear quadratic dynamic games. The PI-based adaptive Q-learning method can learn the feedback Nash equilibrium online using the state samples generated by behavior policies, without sending inquiries to the system model. Unlike the existing Q-learning methods, this novel Q-learning algorithm executes both policy evaluation and policy improvement in an adaptive manner.We prove the convergence of the offline PI algorithm by proving its equivalence to Newton’s method while solving the game algebraic Riccati equation(GARE). Furthermore, we prove that the proposed Q-learning method will converge to the Nash equilibrium under a small learning rate if the method satisfies certain persistence of excitation conditions, which can be easily met by suitable behavior policies. Our simulation results demonstrate the good performance of the proposed online adaptive Q-learning algorithm. | Xinxing LI Zhihong PENG Lei JIAO Lele XI Junqi CAI | 2019 | Science China(Information Sciences)2019，62，12： | 4 |
| 3 | Non-Cooperative Differential Game Based Load Balancing Algorithm in Radio-Over-Fibre System显示文摘 | HE Zhen WANG Jianping | 2014 | China Communications2014，11，A02： | 0 |
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