Poster

Goal Misgeneralization in Deep Reinforcement Learning

Lauro Langosco di Langosco · Jack Koch · Lee Sharkey · Jacob Pfau · David Krueger

Hall E #926

Keywords: [ RL: Inverse ] [ SA: Everything Else ] [ SA: Fairness, Equity, Justice and Safety ] [ DL: Robustness ] [ RL: Deep RL ] [ DL: Everything Else ] [ Deep Learning ] [ RL: Everything Else ]

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Wed 20 Jul 3:30 p.m. PDT — 5:30 p.m. PDT
 
Spotlight presentation: Reinforcement Learning
Wed 20 Jul 1:30 p.m. PDT — 3:05 p.m. PDT

Abstract:

We study \emph{goal misgeneralization}, a type of out-of-distribution robustness failure in reinforcement learning (RL). Goal misgeneralization occurs when an RL agent retains its capabilities out-of-distribution yet pursues the wrong goal. For instance, an agent might continue to competently avoid obstacles, but navigate to the wrong place. In contrast, previous works have typically focused on capability generalization failures, where an agent fails to do anything sensible at test time.We provide the first explicit empirical demonstrations of goal misgeneralization and present a partial characterization of its causes.

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