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Lanting Zeng

2 accepted papers

2024

Differentially Private No-regret Exploration in Adversarial Markov Decision Processes

UAI 2024poster

We study learning adversarial Markov decision process (MDP) in the episodic setting under the constraint of differential privacy (DP). This is motivated by the widespread applications of reinforcement learning (RL) in non-stationary and even adversarial scenarios, where protecting users’ sensitive i…

Cited by 1SourcePDFScholar
2022

Exploring the Vulnerability of Deep Reinforcement Learning-based Emergency Control for Low Carbon Power Systems

IJCAI 2022poster

Decarbonization of global power systems significantly increases the operational uncertainty and modeling complexity that drive the necessity of widely exploiting cutting-edge Deep Reinforcement Learning (DRL) technologies to realize adaptive and real-time emergency control, which is the last resort…