AI RESEARCH
Policy Gradient for Continuous-Time Robust Markov Decision Processes
arXiv CS.LG
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ArXi:2606.04335v1 Announce Type: new The framework of robust Marko decision processes (RMDPs) allows the design of reinforcement learning agents that satisfy performance guarantees under worst-case transition dynamics. Traditional RMDPs consider discrete-time dynamics and recently, sample-efficient policy gradient algorithms have been considered in this context. This paper investigates policy gradient algorithms within a continuous-time RMDP framework.