MoL-2025-03: Skaisgiris, Paulius (2025) Inductive Learning of Temporal Advice Formulae for Guiding Planners. [Report]
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Abstract
Reinforcement learning (RL), one of the most successful methods for planning in stochastic environments, suffers from sample inefficiency, requiring extensive exploration of the environment to converge on good solutions. Additionally, most RL methods function as black boxes, limiting human intervention. This thesis attempts to tackle these problems and presents a method for learning temporal advice formulae to enhance the efficiency, quality, and safety of planning algorithms while maintaining transparency.
We use linear temporal logic on finite traces as a general framework for expressing advice. Inspired by previous works by Meli et al. and Ielo et al., we combine the existing research on learning time-independent advice for planners and inferring formulae from execution traces, rto develop a unified method for learning temporal advice. We represent the temporal logic formulae as answer set programs and use the ILASP software for inductively learning them from execution traces. Unlike previous work, our approach tailors temporal logic formulae for guiding planning agents and accounts for partially observable and noisy domains. This integration enables automated advice generation, aiming to improve decision-making in automated planning.
We experimentally validate our approach in two environments: a simple fully observable gem pickup scenario and RockSample, which involves long planning horizons and partial observability. Our results demonstrate that generalizable temporal advice formulae can be learned from only a few examples, provided they are of high quality and clearly distinguish good from bad behavior.
| Item Type: | Report |
|---|---|
| Report Nr: | MoL-2025-03 |
| Series Name: | Master of Logic Thesis (MoL) Series |
| Year: | 2025 |
| Subjects: | Computation Logic |
| Depositing User: | Dr Marco Vervoort |
| Date Deposited: | 08 May 2025 14:39 |
| Last Modified: | 08 May 2025 14:39 |
| URI: | https://eprints.illc.uva.nl/id/eprint/2362 |
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