Robust Reinforcement Learning for Linear Temporal Logic Specifications with Finite Trajectory Duration
Bibliographic record
Abstract
Linear Temporal Logic (LTL), a formal behavioral specification language, offers a mathematically unambiguous and succinct way to represent operating requirements for a wide variety of Artificial Intelligence (AI) systems, including autonomous and robotic systems. Despite progress, learning policies that reliably satisfy complex LTL specifications in challenging environments remains an open problem. While LTL specifications are evaluated over infinite sequences, this work focuses on solving objectives within a given finite number of steps, as is to be expected in most real-world applications involving robotic or autonomous systems. We study the problem of generating trajectories of a system that satisfy a given LTLf specification in an environment with a priori unknown transition probabilities. Our proposed approach builds upon the popular AlphaGo Zero Reinforcement Learning (RL) framework, which has found great success in the two-player game of Go, to learn policies that can satisfy an LTLf specification given a limit on the trajectory duration. Extensive simulations on complex robot motion planning problems demonstrate that our approach achieves higher success rates in satisfying studied specifications with time constraints compared to state-of-the-art methods. Importantly, our approach succeeds in cases where the baseline method fails to find any satisfying policies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".