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Automating the Refinement of Reinforcement Learning Specifications

Tanmay Ambadkar, Đorđe Žikelić, Abhinav Verma

TL;DR

AutoSpec tackles under-specified reinforcement learning tasks by automatically refining coarse SpectRL specifications into sound, more informative refinements that guide learning. It introduces four refinement operators operating on abstract SpectRL graphs and demonstrates how a wrapper framework can iteratively improve policy satisfaction thresholds when integrated with DiRL and LSTS. The empirical results across 9-rooms, randomized grids, and PandaGym show notable gains in learnability and robustness, including in high-dimensional settings. This approach advances practical specification-guided RL by enabling automated, principled refinement of task specifications while acknowledging inherent limitations in completeness and exploration requirements.

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 \textsc{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. \textsc{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 existing edge specifications or by introducing new edge specifications. We prove that all four procedures maintain specification soundness, i.e. any trajectory satisfying the refined specification also satisfies the original. We then show how \textsc{AutoSpec} can be integrated with existing reinforcement learning algorithms for learning policies from logical specifications. Our experiments demonstrate that \textsc{AutoSpec} yields promising improvements in terms of the complexity of control tasks that can be solved, when refined logical specifications produced by \textsc{AutoSpec} are utilized.

Automating the Refinement of Reinforcement Learning Specifications

TL;DR

AutoSpec tackles under-specified reinforcement learning tasks by automatically refining coarse SpectRL specifications into sound, more informative refinements that guide learning. It introduces four refinement operators operating on abstract SpectRL graphs and demonstrates how a wrapper framework can iteratively improve policy satisfaction thresholds when integrated with DiRL and LSTS. The empirical results across 9-rooms, randomized grids, and PandaGym show notable gains in learnability and robustness, including in high-dimensional settings. This approach advances practical specification-guided RL by enabling automated, principled refinement of task specifications while acknowledging inherent limitations in completeness and exploration requirements.

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 \textsc{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. \textsc{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 existing edge specifications or by introducing new edge specifications. We prove that all four procedures maintain specification soundness, i.e. any trajectory satisfying the refined specification also satisfies the original. We then show how \textsc{AutoSpec} can be integrated with existing reinforcement learning algorithms for learning policies from logical specifications. Our experiments demonstrate that \textsc{AutoSpec} yields promising improvements in terms of the complexity of control tasks that can be solved, when refined logical specifications produced by \textsc{AutoSpec} are utilized.

Paper Structure

This paper contains 27 sections, 1 theorem, 2 equations, 11 figures, 7 algorithms.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Automated Refinement of RL Specifications
  4. Overview of AutoSpec.
  5. Specification Refinement Subprocedures
  6. Correctness of AutoSpec
  7. Incompleteness of the Specification Refinement Problem.
  8. Experimental Evaluation
  9. Experimental Setup
  10. Algorithm-Dependent Effectiveness: DiRL vs LSTS
  11. Evaluation of Individual Refinements
  12. High-Dimensional Validation: PandaGym
  13. Conclusion
  14. Appendix / supplemental material
  15. Refinement Algorithms
  16. ...and 12 more sections

Key Result

Theorem 1

Given an abstract graph $G$ of a SpectRL specification $\phi$ and an edge $e$, AutoSpec computes a specification $\phi_r$ and returns an abstract graph $G_r$ and an edge $e_r$ such that $\phi_r$ refines $\phi$. That is, for any MDP trajectory $\zeta$, we have $(\zeta \models \phi_r) \implies (\zeta

Figures (11)

  • Figure 1: Example of refinement by AutoSpec in a 9-rooms environment. The original MID-node region includes a trap state from which recovery is impossible. The refined specification excludes this trap, enabling the agent to learn a policy with higher satisfaction probability.
  • Figure 2: Illustrations of abstract graph refinement processes. (a) ReachRefine demonstrating removal of failed part of goal region and addition of unsafe states set to existing avoid. (b) AddRefine demonstrating addition of waypoint between 2 nodes. (c) PastRefine removes part of the source node from where the agent failed (red) to get to the target, and keeps successful start states (green). (d) OrRefine shows how alternative paths (dotted lines) to target are constructed using existing specification nodes.
  • Figure 3: 100-rooms Environment with marked regions its DAG specification
  • Figure 4: Task satisfiability curves representing performances of DIRL and LSTS for sub-specifications and complete specification
  • Figure 5: Evaluation of AutoSpec on PandaGym: (a) Two perspectives of the environment (1st and 2nd Figures), where the red region is an intermediate goal and an invisible wall blocks direct paths. (b) Performance of DiRL with and without AutoSpec: ReachRefine on first edge (3rd Figure) and PastRefine on second edge (4th Figure).
  • ...and 6 more figures

Theorems & Definitions (3)

  • Definition 1: Abstract graph
  • Definition 2: Specification refinement
  • Theorem 1: Correctness of AutoSpec