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Goal Misgeneralization in Deep Reinforcement Learning
Lauro Langosco di Langosco · Jack Koch · Lee Sharkey · Jacob Pfau · David Krueger

Wed Jul 20 02:55 PM -- 03:00 PM (PDT) @ Hall G
in Reinforcement Learning »

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.

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

Author Information

Lauro Langosco di Langosco (University of Cambridge)
Jack Koch (Unaffiliated)
Lee Sharkey (University of Tuebingen)
Jacob Pfau (University of Edinburgh)
David Krueger (University of Cambridge)

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