Beyond Value: CHECKLIST for Testing Inferences in Planning-Based RL

• URL: http://arxiv.org/abs/2206.02039v2
• Date: Tue, 7 Jun 2022 20:41:50 GMT
• Title: Beyond Value: CHECKLIST for Testing Inferences in Planning-Based RL
• Authors: Kin-Ho Lam, Delyar Tabatabai, Jed Irvine, Donald Bertucci, Anita Ruangrotsakun, Minsuk Kahng, Alan Fern
• Abstract summary: Reinforcement learning (RL) agents are commonly evaluated via their expected value over a distribution of test scenarios. We consider testing RL agents that make decisions via online tree search using a learned transition model and value function. 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.
• Score: 20.360392791376707
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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