RankQ: A New Offline-to-Online RL Method Using Self-Supervised Action Ranking
A new offline-to-online reinforcement learning (RL) algorithm called RankQ has been introduced by researchers to tackle the issue of value overestimation in extensive state-action environments. Instead of uniformly penalizing actions that are out-of-distribution, RankQ focuses on learning relative preferences among actions. By enhancing temporal-difference learning with a self-supervised multi-term ranking loss, it establishes a structured order for actions, which aids in refining policies towards more effective behaviors. This strategy circumvents the behavior cloning anchor effect found in earlier pessimistic approaches that may obstruct online enhancements when actions from the dataset are less than optimal. The findings are available on arXiv (2605.11151) and aim to boost sample efficiency by utilizing pre-collected datasets prior to online engagement.
Key facts
- RankQ is an offline-to-online RL algorithm.
- It uses a self-supervised multi-term ranking loss.
- The method augments temporal-difference learning.
- It enforces structured action ordering.
- It avoids uniform penalization of OOD actions.
- Prior methods impose pessimism by down-weighting OOD actions.
- The approach mitigates value overestimation.
- The paper is on arXiv with ID 2605.11151.
Entities
Institutions
- arXiv