Hasan Ferit Eniser, Songtuan Lin, Nicola Müller, Anastasia Isychev, Valentin Wüstholz, Isabel Valera, Jörg Hoffmann, Maria Christakis

Using Action-Policy Testing in RL to Reduce the Number of Bugs

Introduction

Using action-policy testing in rl to reduce the number of bugs. Reduce bugs in Reinforcement Learning (RL) with action-policy testing. Optimize training by identifying sub-optimal states, leading to improved performance & fewer errors.

Abstract

Reinforcement learning is becoming ever more prominent in solving combinatorial search problems, in particular ones where states are images. Prior work has devised action-policy testing methodology, that identifies so-called bug states where policy performance is sub-optimal. Here we show how to leverage this methodology during the RL process, using action-policy testing to find bugs and injecting those as alternate start states for the training runs. Running experiments across six 2D games, we find that our testing-guided training often achieves similar expected reward while reducing the number of bugs.

Review

This paper presents a novel approach to improving the robustness of Reinforcement Learning policies by integrating action-policy testing directly into the training process. The core idea is to leverage an existing methodology for identifying "bug states"—where policy performance is sub-optimal—and re-inject these identified states as alternate starting points for subsequent training runs. This feedback loop aims to address the common issue of RL policies exhibiting sub-optimal behavior, particularly in complex environments where states might be represented as images. The authors demonstrate their method's effectiveness across six 2D games, reporting that this testing-guided training can achieve comparable expected rewards while significantly reducing the number of bugs encountered by the learned policies. The proposed methodology offers a compelling strategy for enhancing the reliability and trustworthiness of RL agents. By proactively identifying and addressing "bug states" during training, the approach directly tackles a critical practical challenge in deploying RL systems, especially in scenarios demanding high levels of robustness or safety. The concept of using testing not just for evaluation but as an active component of the learning process is innovative and has the potential to make RL training more efficient in producing high-quality, stable policies. The experimental evidence across multiple 2D games lends initial credibility to the method's generalizability within that domain, suggesting a robust improvement in policy quality with respect to error reduction. While the abstract outlines a promising direction, several aspects would benefit from further elaboration in the full paper. It would be valuable to understand the precise nature of the "bugs" identified and whether the reported "similar expected reward" indicates no performance gain in optimal scenarios, only a reduction in sub-optimalities, or if there is a trade-off involved. The computational overhead of running the action-policy testing alongside or intermittently during training is also a critical consideration. Furthermore, insights into the characteristics of the six 2D games—their complexity, state-space size, and reward structures—would help contextualize the results and provide a clearer indication of the method's potential for scalability to more complex, real-world applications beyond the tested domain.

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