Auction-Based Online Policy Adaptation for Evolving Objectives
We consider multi-objective reinforcement learning problems where objectives come from an identical family -- such as the class of reachability objectives -- and may appear or disappear at runtime. Our goal is to design adaptive policies that can efficiently adjust their behaviors as the set of active objectives changes. To solve this problem, we propose a modular framework where each objective is supported by a selfish local policy, and coordination is achieved through a novel auction-based mechanism: policies bid for the right to execute their actions, with bids reflecting the urgency of the current state. The highest bidder selects the action, enabling a dynamic and interpretable trade...
| Authors | Guruprerana Shabadi, Kaushik Mallik |
|---|---|
| Primary category | cs.LG |
| Published | 2 April 2026 |
| Last updated at source | 10 May 2026 |
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