In this paper, we address the following problem: Given an offline demonstration dataset from an imperfect expert, what is the best way to leverage it to bootstrap online learning performance in MDPs. We first propose an Informed Posterior Sampling-based RL (iPSRL) algorithm that uses the offline dataset, and information about the expert's behavioral policy used to generate the offline dataset. Its cumulative Bayesian regret goes down to zero exponentially fast in N, the offline dataset size if the expert is competent enough. Since this algorithm is computationally impractical, we then propose the iRLSVI algorithm that can be seen as a combination of the RLSVI algorithm for online RL, and imitation learning. Our empirical results show that the proposed iRLSVI algorithm is able to achieve significant reduction in regret as compared to two baselines: no offline data, and offline dataset but used without information about the generative policy. Our algorithm bridges online RL and imitation learning for the first time.

翻译：本文探讨以下问题：给定一个来自非完美专家的离线演示数据集，如何最优地利用它来启动马尔可夫决策过程中的在线学习性能？我们首先提出一种基于知情后验采样的强化学习算法（iPSRL），该算法利用离线数据集及生成该数据集的专家行为策略信息。若专家具备足够能力，其累积贝叶斯遗憾值将随离线数据集规模N呈指数级下降至零。由于该算法计算不可行，我们继而提出iRLSVI算法，可视为在线强化学习算法RLSVI与模仿学习的结合。实验结果表明，与两种基线（无离线数据、使用离线数据但未利用生成策略信息）相比，所提出的iRLSVI算法能够显著降低遗憾值。本算法首次实现了在线强化学习与模仿学习的桥梁连接。