atlas

Research map / Bandits and online learning / RL theory and MDPs

Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR

ICML 2023 · Bandits and online learning › RL theory and MDPs · approach: Regret bounds with function approximation · 1 citations (OpenAlex)

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Bernstein bonus (with sqrt(tau) variance term and 1/N term) instead of Hoeffding bonus in CVaR UCBCVaR MAB (Bernstein-UCB) · MAB CVaR regretregret rateHelped
Bernstein bonus instead of Hoeffding bonus in CVaR-UCBVItabular CVaR RL (CVaR-UCBVI) · RL CVaR regretleading term in regret bound: τ^-1√(SAHK) to τ^-1√(SAK), an S√H improvementHelped
Assume continuous returns with lower-bounded densityCVaR-UCBVI with Bernstein bonus · RL CVaR regretleading term in regret bound: τ^-1√(SAK) to √(τ^-1SAK), matching lower boundHelped
Discretize rewards to an η-grid (η=1/√K) for computational efficiencyCVaR-UCBVI · RL CVaR regretregret and runtimeNo clear effect

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy