针对小数据集下的离线强化学习过拟合问题，提出稀疏正则化方法提升样本效率。
Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity
- 基于稀疏性设计正则化项，抑制小数据下的过拟合
- 在连续控制任务中优于当前最优基线模型
- 适用于数据量有限的实际离线强化学习场景
本文研究了在离线强化学习（Offline RL）中使用小规模数据集的挑战。尽管多数常见基准使用超过百万条数据，但许多实际应用依赖更小的数据集。我们发现，现有离线RL算法在小数据上容易过拟合，导致性能下降。为此，提出“Sparse-Reg”：一种基于稀疏性的正则化技术，有效缓解离线强化学习中的过拟合问题，支持在数据受限条件下实现高效学习，并在连续控制任务中超越当前最先进的基线方法。
原文摘要 · Abstract (English)
In this paper, we investigate the use of small datasets in the context of offline reinforcement learning (RL). While many common offline RL benchmarks employ datasets with over a million data points, many offline RL applications rely on considerably smaller datasets. We show that offline RL algorithms can overfit on small datasets, resulting in poor performance. To address this challenge, we introduce "Sparse-Reg": a regularization technique based on sparsity to mitigate overfitting in offline reinforcement learning, enabling effective learning in limited data settings and outperforming state-of-the-art baselines in continuous control.
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