most citedHuman-in-the-Loop Policy Optimization for Preference-Based Multi-Objective Reinforcement Learning

2 citations · 3 across the 2 of their papers we have counts for

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2 papers

cs.NE2024★ 2 cited

Human-in-the-Loop Policy Optimization for Preference-Based Multi-Objective Reinforcement Learning

Ke Li, Han Guo

Multi-objective reinforcement learning (MORL) aims to find a set of high-performing and diverse policies that address trade-offs between multiple conflicting objectives. However, i…

cs.AI2023★ 1 cited

Direct Preference-Based Evolutionary Multi-Objective Optimization with Dueling Bandit

Tian Huang, Shengbo Wang, Ke Li

Optimization problems find widespread use in both single-objective and multi-objective scenarios. In practical applications, users aspire for solutions that converge to the region…