Objective Robustness in Deep Reinforcement Learning
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We study objective robustness failures, a type of out-of-distribution robustness failure in reinforcement learning (RL). Objective robustness failures occur when an RL agent retains its capabilities off-distribution yet pursues the wrong objective. We provide the first explicit empirical demonstrations of objective robustness failures and argue that this type of failure is critical to address.
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