arXiv:2605.22711v1 Announce Type: new Abstract: Markov Decision Processes (MDPs) often exhibit significant redundancy due to symmetries and shared structure across state-goal pairs in real-world Goal-Conditioned Reinforcement Learning (GCRL). While hierarchical policies have been motivated for horizon reduction via temporal abstraction in offline GCRL, we demonstrate that hierarchy also enables absolute abstraction. By introducing relativised options as well as distinct representations for different levels of the hierarchy, we demonstrate how an agent can reuse experience across similar contex
This paper leverages significant recent advancements in offline reinforcement learning and the increasing focus on sample efficiency in real-world AI applications.
Improving abstraction and reusability in goal-conditioned reinforcement learning directly accelerates the development of more capable and efficient AI agents, particularly in complex environments.
This research introduces concrete methods for hierarchical abstraction in offline GCRL, enabling agents to learn more effectively from limited datasets and generalize across diverse tasks by reusing experiences.
- · AI/ML researchers
- · Robotics companies
- · Developers of autonomous systems
- · Tasks requiring extensive hand-coded policies
- · Systems heavily reliant on online data collection
More robust and generalizable AI agents can be trained with less data.
This reduces the cost and time required to deploy AI solutions in new, complex domains.
Accelerated development of autonomous systems could lead to new industries and significant productivity gains across sectors.
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Read at arXiv cs.LG