Most provably-efficient learning algorithms introduce optimism about poorly-understood states and actions to encourage exploration. We study an alternative approach for efficient exploration, \emph{posterior sampling for reinforcement learning} (PSRL). This algorithm proceeds in repeated episodes of known duration. At the start of each episode, PSRL updates a prior distribution over Markov decision processes and takes one sample from this posterior. PSRL then follows the policy that is optimal for this sample during the episode. The algorithm is conceptually simple, computationally efficient and allows an agent to encode prior knowledge in a natural way. We establish an $\tilde{O}(\tau S \sqrt{AT})$ bound on the expected regret, where $T$ is time, $\tau$ is the episode length and $S$ and $A$ are the cardinalities of the state and action spaces. This bound is one of the first for an algorithm not based on optimism, and close to the state of the art for any reinforcement learning algorithm. We show through simulation that PSRL significantly outperforms existing algorithms with similar regret bounds.

该研究提出了一种用于强化学习的后验采样方法（PSRL），通过对一个先验分布进行贝叶斯更新来在已知的一系列时段内实现对Markov决策过程的优化，从而达到高效的探索。该算法在时间，状态和行动空间上有明显的性能优势，并具有一定的先验知识编码能力。

通过后验抽样实现（更）高效的强化学习