Automating the Refinement of Reinforcement Learning Specifications
Tanmay Ambadkar, {\DJ}or{\dj}e \v{Z}ikeli\'c, Abhinav Verma
TL;DR
AutoSpec is a framework that refines logical specifications for reinforcement learning, making complex tasks easier to learn by guiding agents with improved, sound specifications.
Contribution
We introduce AutoSpec, a novel method for automatically refining logical specifications to enhance reinforcement learning performance.
Findings
AutoSpec improves the complexity of solvable control tasks.
Refined specifications lead to better learning efficiency.
All refinement procedures maintain specification soundness.
Abstract
Logical specifications have been shown to help reinforcement learning algorithms in achieving complex tasks. However, when a task is under-specified, agents might fail to learn useful policies. In this work, we explore the possibility of improving coarse-grained logical specifications via an exploration-guided strategy. We propose AutoSpec, a framework that searches for a logical specification refinement whose satisfaction implies satisfaction of the original specification, but which provides additional guidance therefore making it easier for reinforcement learning algorithms to learn useful policies. AutoSpec is applicable to reinforcement learning tasks specified via the SpectRL specification logic. We exploit the compositional nature of specifications written in SpectRL, and design four refinement procedures that modify the abstract graph of the specification by either refining its…
Peer Reviews
Decision·ICLR 2026 Poster
- Problem statement is relevant, interesting, and well defined. - The authors do a good job of explaining the preliminary material needed to understand their work. - The results show that their method significantly improves the performance of the re-trained policies to meet the original specification after refinement.
- Some of the presentation of the AutoSpec framework is lacking... specifically Figure 2 really doesn't clarify what the PastRefine refinement procedure does. What's the relationship between the two parts of the figure? It also leaves a ton of open whitespace, which looks sloppy. - It would be nice to have visuals on how each of the refinement procedures is working, not just PastRefine. - The experimentation and presentation of it is lacking significantly - Only use 2 experimental setups (n-Roo
- The paper addresses an important challenge in formulating specifications for RL. Automatically refining predicates and specifications provided to the agent is a promising direction, particularly because crafting appropriate predicates is difficult and loosely defined specifications can be hard to satisfy. - The framework is integrated with established specification guided algorithms and the experiments illustrate how the refinements interact with the different exploration strategies of $\mathr
- The empirical scope is narrow. Only two domains are considered, n Rooms and PandaGym. This limits the evidence for scalability and diversity of specifications. A broader study that samples many predicate regions or includes a less contrived multi room world would strengthen the case. - The specifications tested upon are quite limited (only 1 or 2 refinements per specification needed). A sample driven testing approach (say randomly chosen predicate regions) or a less contrived 100 room exam
This is one of the first few works to consider the problem of automatic refinement of RL specifications based on collected feedback from the training of policies. This is a fundamental issue because if the specification is too coarse grained then algorithms would find it hard to effectively explore the state space and learn good policies. The problem they consider is studied in depth and many refinement techniques are proposed that are sound. The benchmarks they consider are also interesting. T
While the contributions of the paper are substantive, they can be presented better. The introduction can be expanded to give further intuition with respect to the problem being solved. Specifically, the notion of abstract graph is never introduced informally even though it is central to the paper. Perhaps it would be helpful to take the example in figure 1, and present in some detail about what the refinement procedures would produce and how it would make the learning task easier. Similarly, log
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Taxonomy
TopicsFormal Methods in Verification · Reinforcement Learning in Robotics · Machine Learning and Algorithms