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Cascaded Gaps: Towards Logarithmic Regret for Risk-Sensitive Reinforcement Learning

ICML 2022 · Bandits and online learning › RL theory and MDPs · approach: Regret bounds with function approximation

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace gap-independent analysis with cascaded-gap (gap-dependent) analysis for RSVI2 and RSQ2RSVI2 and RSQ2 under entropic risk · episodic finite-horizon tabular MDPregret scaling in KHelped
Use the risk-dependent normalization factor ψβ in the cascaded gap instead of sign(β)cascaded gap definition · risk-sensitive MDP with rewards 1 except sub-optimal final-step actionsmagnitude consistency of gap across signs of βHelped
Small risk regime |β| ≤ 1/H versus large riskregret bounds for RSVI2 and RSQ2 · episodic risk-sensitive MDPregret bound dependence on βHelped
Large risk (|β|(H−1) large) leads to exponential dependence on |β|H in upper and lower boundsgap-dependent regret · episodic risk-sensitive MDPregret bound exponential factorHurt

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