arXiv:2602.05459v2 Announce Type: replace Abstract: Offline goal-conditioned reinforcement learning (GCRL) is typically benchmarked by the best tuned success rate of each method. This score measures attainable performance, but it does not reveal how reliably a learned goal-conditioned signal can be extracted into a policy: a method could succeed across many value-learning and extraction settings, or only at a narrow, hard-to-find configuration. We study this gap across four methods, GCIQL, GCIVL, QRL, and CRL, under a shared advantage-weighted regression (AWR) extractor. For each method, we co
The increasing complexity and practical deployment of offline reinforcement learning methods necessitate more robust evaluation metrics beyond simple success rates.
This research refines the understanding and implementation of GCRL, which is crucial for developing reliable and broadly applicable AI agents in complex environments.
The focus of GCRL evaluation shifts from mere attainable performance to the broader concepts of trainability and extractability, leading to more robust and reliable AI systems.
- · AI developers
- · Robotics companies
- · Industrial automation
- · Research institutions
- · AI methods with poor generalization
- · Systems relying on narrowly tuned models
Improved methodologies for evaluating and deploying goal-conditioned reinforcement learning systems.
Faster development and adoption of AI agents capable of operating effectively in diverse, real-world conditions.
Enhanced automation and task capabilities across various sectors, potentially including advanced robotics and autonomous systems.
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Read at arXiv cs.LG