Beyond Value: CheckList for Testing Inferences in Planning-Based RL
DOI:
https://doi.org/10.1609/icaps.v32i1.19848Keywords:
Testing, Reinforcement Learning, TrustAbstract
Reinforcement learning (RL) agents are commonly evaluated via their expected value over a distribution of test scenarios. Unfortunately, this evaluation approach provides limited evidence for post-deployment generalization beyond the test distribution. In this paper, we address this limitation by extending the recent CheckList testing methodology from natural language processing to planning-based RL. Specifically, we consider testing RL agents that make decisions via online tree search using a learned transition model and value function. The key idea is to improve the assessment of future performance via a CheckList approach for exploring and assessing the agent's inferences during tree search. The approach provides the user with an interface and general query-rule mechanism for identifying potential inference flaws and validating expected inference invariances. We present a user study involving knowledgeable AI researchers using the approach to evaluate an agent trained to play a complex real-time strategy game. The results show the approach is effective in allowing users to identify previously-unknown flaws in the agent's reasoning. In addition, our analysis provides insight into how AI experts use this type of testing approach, which may help improve future instantiations.Downloads
Published
2022-06-13
How to Cite
Lam, K.-H., Tabatabai, D., Irvine, J., Bertucci, D., Ruangrotsakun, A., Kahng, M., & Fern, A. (2022). Beyond Value: CheckList for Testing Inferences in Planning-Based RL. Proceedings of the International Conference on Automated Planning and Scheduling, 32(1), 606-614. https://doi.org/10.1609/icaps.v32i1.19848
Issue
Section
Planning and Learning Track