Shengde Jia

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Markov decision process (MDP) is a foundational framework of reinforcement learning advanced in sequential decision problems. Continuous-time Markov decision process (CTMDP) extends the discrete time MDP model by allowing actions to take place at any time. Prior work has little consideration on the reinforcement learning methods for solving CTMDPs. The aim(More)
This paper presents a novel method-continuous-time Markov decision process (CTMDP)-to address the uncertainties in pursuit-evasion problem. The primary difference between the CTMDP and the Markov decision process (MDP) is that the former takes into account the influence of the transition time between the states. The policy iteration method-based potential(More)
This paper addresses the auto landing problem of a quadrotor unmanned aerial vehicle (UAV) equipped with a single down-looking vertically camera on the ground target using image-based visual servo (IBVS) control. Observable features on a ground guidance cooperation mark flat are exploited to recognize and obtain the center pixel position of the mark,(More)
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