SlowGuess/NeurIPS_2023_OCR huggingface dataset:
and education, where the data collection processes are often costly or risky. However, it is important to acknowledge the potential negative societal impacts associated with any offline RL algorithm. One such concern is that the offline data used for training may contain inherent biases, and these biases can potentially transfer to the learned policy. Additionally, it is worth considering the potential impact of offline RL on employment, as it contributes to the automation of tasks traditionally performed by human experts, such as factory automation or autonomous driving. Addressing these challenges will contribute to the responsible development and deployment of offline RL algorithms, maximizing their positive impact while minimizing negative societal consequences.
Academic. We reexamine the fundamental objective of value regularization in offline RL and propose supported value regularization, which not only offers enhanced theoretical guarantees but also demonstrates remarkable improvements over existing methods on widely recognized offline RL benchmarks. This research potentially offers researchers a novel perspective and a promising avenue for exploring value regularization and achieving support-constrained optimality in offline RL.