Timezone: »
Existing observational approaches for learning human preferences, such as inverse reinforcement learning, usually make strong assumptions about the observability of the human's environment. However, in reality, people make many important decisions under uncertainty. To better understand preference learning in these cases, we study the setting of inverse decision theory (IDT), a previously proposed framework where a human is observed making non-sequential binary decisions under uncertainty. In IDT, the human's preferences are conveyed through their loss function, which expresses a tradeoff between different types of mistakes. We give the first statistical analysis of IDT, providing conditions necessary to identify these preferences and characterizing the sample complexity—the number of decisions that must be observed to learn the tradeoff the human is making to a desired precision. Interestingly, we show that it is actually easier to identify preferences when the decision problem is more uncertain. Furthermore, uncertain decision problems allow us to relax the unrealistic assumption that the human is an optimal decision maker but still identify their exact preferences; we give sample complexities in this suboptimal case as well. Our analysis contradicts the intuition that partial observability should make preference learning more difficult. It also provides a first step towards understanding and improving preference learning methods for uncertain and suboptimal humans.
Author Information
Cassidy Laidlaw (University of California, Berkeley)
Stuart Russell (UC Berkeley)
Related Events (a corresponding poster, oral, or spotlight)
-
2021 Spotlight: Uncertain Decisions Facilitate Better Preference Learning »
Dates n/a. Room
More from the Same Authors
-
2021 : An Empirical Investigation of Representation Learning for Imitation »
Cynthia Chen · Sam Toyer · Cody Wild · Scott Emmons · Ian Fischer · Kuang-Huei Lee · Neel Alex · Steven Wang · Ping Luo · Stuart Russell · Pieter Abbeel · Rohin Shah -
2021 : Cross-Domain Imitation Learning via Optimal Transport »
Arnaud Fickinger · Samuel Cohen · Stuart Russell · Brandon Amos -
2022 : Adversarial Policies Beat Professional-Level Go AIs »
Tony Wang · Adam Gleave · Nora Belrose · Tom Tseng · Michael Dennis · Yawen Duan · Viktor Pogrebniak · Joseph Miller · Sergey Levine · Stuart Russell -
2021 : BASALT: A MineRL Competition on Solving Human-Judged Task + Q&A »
Rohin Shah · Cody Wild · Steven Wang · Neel Alex · Brandon Houghton · William Guss · Sharada Mohanty · Stephanie Milani · Nicholay Topin · Pieter Abbeel · Stuart Russell · Anca Dragan -
2021 Poster: Scalable Online Planning via Reinforcement Learning Fine-Tuning »
Arnaud Fickinger · Hengyuan Hu · Brandon Amos · Stuart Russell · Noam Brown -
2021 Poster: Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism »
Paria Rashidinejad · Banghua Zhu · Cong Ma · Jiantao Jiao · Stuart Russell -
2021 Poster: MADE: Exploration via Maximizing Deviation from Explored Regions »
Tianjun Zhang · Paria Rashidinejad · Jiantao Jiao · Yuandong Tian · Joseph Gonzalez · Stuart Russell -
2020 Workshop: Navigating the Broader Impacts of AI Research »
Carolyn Ashurst · Rosie Campbell · Deborah Raji · Solon Barocas · Stuart Russell -
2020 Poster: The MAGICAL Benchmark for Robust Imitation »
Sam Toyer · Rohin Shah · Andrew Critch · Stuart Russell -
2020 Poster: SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory »
Paria Rashidinejad · Jiantao Jiao · Stuart Russell -
2020 Oral: SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory »
Paria Rashidinejad · Jiantao Jiao · Stuart Russell -
2020 Poster: Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design »
Michael Dennis · Natasha Jaques · Eugene Vinitsky · Alexandre Bayen · Stuart Russell · Andrew Critch · Sergey Levine -
2020 Oral: Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design »
Michael Dennis · Natasha Jaques · Eugene Vinitsky · Alexandre Bayen · Stuart Russell · Andrew Critch · Sergey Levine -
2019 Poster: Functional Adversarial Attacks »
Cassidy Laidlaw · Soheil Feizi -
2018 Poster: Meta-Learning MCMC Proposals »
Tongzhou Wang · YI WU · Dave Moore · Stuart Russell -
2018 Poster: Learning Plannable Representations with Causal InfoGAN »
Thanard Kurutach · Aviv Tamar · Ge Yang · Stuart Russell · Pieter Abbeel