MDP

Event Weekly OptiML Lab Group Meeting Short summary In this seminar, I will talk about my own paper “Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion” (Lee et al.

Event Weekly OptiML Lab Group Meeting Short summary In this talk, I will first (somewhat rigorously) introduce the framework of reinforcement learning with human feedback (RLHF). Then I will go over three recent breakthroughs in the analysis and improvement of RLHF.

Event Weekly OSI Lab Seminar Short summary In this talk, I will first (somewhat rigorously) introduce the framework of reinforcement learning with human feedback (RLHF). Then I will go over three recent breakthroughs in the analysis and improvement of RLHF.

Event Weekly OSI Lab Seminar Short summary In this seminar, I will talk about the paper “Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs” (Cheng et al., ICLR 2023).

Event Weekly OptiML Lab Group Meeting Short summary In this seminar, I will talk about my own paper “Nearly Optimal Latent State Decoding in Block MDPs” (Jedra et al., arXiv 2022).

Event Weekly OSI Lab Seminar Short summary In this seminar, I will talk about the paper “Clustering in Block Markov Chains” (Sanders et al., Ann. Stat. 2020). Abstract (taken directly from the paper)

We show that a simple trick of randomly corrupting the trajectories in Block MDPs allow for us to use the the clustering algorithm proposed of Jedra et al. (2023) for general classes of MDPs.

First theoretical analysis of model estimation and reward-free RL of block MDP, without resorting to function approximation frameworks. Lower bounds and algorithms with near-optimal upper bound are provided.

We empirically validate the clustering algorithm proposed in (Jedra et al., 2022).

(tbd)