Beyond Value: CHECKLIST for Testing Inferences in Planning-Based RL. (arXiv:2206.02039v2 [cs.AI] UPDATED)

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 …

ai arxiv planning rl testing value