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This research introduces ERAHBO, a Bayesian optimization technique designed for hyperparameter tuning in reinforcement learning by explicitly modeling outcome variance alongside mean performance. By incorporating risk-averse constraints into the sampling strategy, the method improves sample efficiency and stabilizes training across diverse RL environments compared to standard baselines. The work advances machine learning infrastructure by providing tools that make complex stochastic tasks more reliable without requiring excessive computational resources.
Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL
arXiv:2607.26680v1 Announce Type: cross Abstract: Reinforcement learning (RL) has shown remarkable success across a wide range of complex tasks. However, RL outcomes can be highly stochastic, and both expected performance and variability often depend on hyperparameter (HP) configurations. We propose efficient and risk-averse heteroscedastic Bayesian Optimization (ERAHBO), a Bayesian optimization method that models both the mean and variance of learning outcomes as functions of the HP configurations. ERAHBO aims to identify HP configurations that achieve high average return while reducing variability across training runs, and it improves the sample efficiency of the HP optimization via adaptive re-sampling rather than a fixed budget per HP. Empirical evaluations across diverse RL algorithms and environments demonstrate that ERAHBO generally outperforms both risk-neutral and risk-averse baselines, delivering improved sample efficiency for risk-averse returns.
Abstract:Reinforcement learning (RL) has shown remarkable success across a wide range of complex tasks. However, RL outcomes can be highly stochastic, and both expected performance and variability often depend on hyperparameter (HP) configurations. We propose efficient and risk-averse heteroscedastic Bayesian Optimization (ERAHBO), a Bayesian optimization method that models both the mean and variance of learning outcomes as functions of the HP configurations. ERAHBO aims to identify HP configurations that achieve high average return while reducing variability across training runs, and it improves the sample efficiency of the HP optimization via adaptive re-sampling rather than a fixed budget per HP. Empirical evaluations across diverse RL algorithms and environments demonstrate that ERAHBO generally outperforms both risk-neutral and risk-averse baselines, delivering improved sample efficiency for risk-averse returns.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2607.26680 [cs.LG]
(or arXiv:2607.26680v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.26680 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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