Reinforcement Learning

Posterior sampling with delayed feedback for reinforcement learning with linear function approximation
(NeurIPS 2023) We provide the first theoretical analysis for the class of posterior sampling algorithms to handle delayed feedback in RL frameworks.
Nikki Lijing Kuang*, Ming Yin*, Mengdi Wang, Yu-Xiang Wang, Yi-An Ma
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Posterior sampling with delayed feedback for reinforcement learning with linear function approximation
Langevin Thompson sampling with logarithmic communication: bandits and reinforcement learning
(ICML 2023) We study approximate Thompson Sampling with Markov Chain Monte Carlo in bandit and reinforcement learning frameworks, providing algorithms that achieve optimal performance with low computation and communication cost.
Nikki Lijing Kuang*, Siddharth Mitra*, Amin Karbasi, Yi-An Ma
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Langevin Thompson sampling with logarithmic communication: bandits and reinforcement learning
Sample Efficient Constrained Reinforcement Learning
We propose sample efficient algorithms for constrained RL frameworks.
Nikki Lijing Kuang
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Sample Efficient Constrained Reinforcement Learning