On The Fragility of Learned Reward FunctionsDownload PDF

08 Oct 2022, 17:47 (modified: 09 Dec 2022, 14:31)Deep RL Workshop 2022Readers: Everyone
Keywords: Deep Reinforcement Learning, Inverse Reinforcement Learning, Reward Learning
TL;DR: An investigation of what causes learned reward functions to fail to train new reinforcement learning agents from scratch.
Abstract: Reward functions are notoriously difficult to specify, especially for tasks with complex goals. Reward learning approaches attempt to infer reward functions from human feedback and preferences. Prior works on reward learning have mainly focused on the performance of policies trained alongside the reward function. This practice, however, may fail to detect learned rewards that are not capable of training new policies from scratch and thus do not capture the intended behavior. Our work focuses on demonstrating and studying the causes of these relearning failures in the domain of preference-based reward learning. We demonstrate with experiments in tabular and continuous control environments that the severity of relearning failures can be sensitive to changes in reward model design and the trajectory dataset composition. Based on our findings, we emphasize the need for more retraining-based evaluations in the literature.
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