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Found 4 papers, 1 papers with code
Dueling Optimization with a Monotone Adversary
We introduce and study the problem of dueling optimization with a monotone adversary, which is a generalization of (noiseless) dueling convex optimization.
Pessimism for Offline Linear Contextual Bandits using $\ell_p$ Confidence Sets
We present a family $\{\hat{\pi}\}_{p\ge 1}$ of pessimistic learning rules for offline learning of linear contextual bandits, relying on confidence sets with respect to different $\ell_p$ norms, where $\hat{\pi}_2$ corresponds to Bellman-consistent pessimism (BCP), while $\hat{\pi}_\infty$ is a novel generalization of lower confidence bound (LCB) to the linear setting.
Exponential Family Model-Based Reinforcement Learning via Score Matching
We propose an optimistic model-based algorithm, dubbed SMRL, for finite-horizon episodic reinforcement learning (RL) when the transition model is specified by exponential family distributions with $d$ parameters and the reward is bounded and known.
Understanding the Eluder Dimension
We provide new insights on eluder dimension, a complexity measure that has been extensively used to bound the regret of algorithms for online bandits and reinforcement learning with function approximation.
