DERAIL: Diagnostic Environments for Reward And Imitation Learning

• URL: http://arxiv.org/abs/2012.01365v1
• Date: Wed, 2 Dec 2020 18:07:09 GMT
• Title: DERAIL: Diagnostic Environments for Reward And Imitation Learning
• Authors: Pedro Freire, Adam Gleave, Sam Toyer, Stuart Russell
• Abstract summary: We develop a suite of diagnostic tasks that test individual facets of algorithm performance in isolation. Results confirm that algorithm performance is highly sensitive to implementation details. Case-study shows how the suite can pinpoint design flaws and rapidly evaluate candidate solutions.
• Score: 9.099589602551573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The objective of many real-world tasks is complex and difficult to procedurally specify. This makes it necessary to use reward or imitation learning algorithms to infer a reward or policy directly from human data. Existing benchmarks for these algorithms focus on realism, testing in complex environments. Unfortunately, these benchmarks are slow, unreliable and cannot isolate failures. As a complementary approach, we develop a suite of simple diagnostic tasks that test individual facets of algorithm performance in isolation. We evaluate a range of common reward and imitation learning algorithms on our tasks. Our results confirm that algorithm performance is highly sensitive to implementation details. Moreover, in a case-study into a popular preference-based reward learning implementation, we illustrate how the suite can pinpoint design flaws and rapidly evaluate candidate solutions. The environments are available at https://github.com/HumanCompatibleAI/seals .

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