论文标题
对约束增强学习的后验抽样的经验评估
An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement Learning
论文作者
论文摘要
我们研究了在受约束的增强学习中有效探索的后验采样方法。另外，我们提出了两种简单的算法，这些算法在统计学上更有效，更简单地实现和计算便宜。第一种算法基于CMDP的线性公式，第二算法利用了CMDP的鞍点公式。我们的经验结果表明，尽管具有简单性，但后取样可实现最先进的表现，在某些情况下，采样明显优于乐观算法。
We study a posterior sampling approach to efficient exploration in constrained reinforcement learning. Alternatively to existing algorithms, we propose two simple algorithms that are more efficient statistically, simpler to implement and computationally cheaper. The first algorithm is based on a linear formulation of CMDP, and the second algorithm leverages the saddle-point formulation of CMDP. Our empirical results demonstrate that, despite its simplicity, posterior sampling achieves state-of-the-art performance and, in some cases, significantly outperforms optimistic algorithms.
