论文标题
在实践强化学习方面：可证明的鲁棒性，可伸缩性和统计效率
On Practical Reinforcement Learning: Provable Robustness, Scalability, and Statistical Efficiency
论文作者
论文摘要
该论文严格研究了现代实际考虑因素的基本强化学习（RL）方法，包括具有神经功能近似的强大RL，分布RL和离线RL。该论文首先为读者准备了RL和统计和优化的关键技术背景的整体概述。在每个设置中，论文都会激发要研究的问题，审查当前文献，提供具有可证明的效率保证的计算有效算法，并以未来的研究方向结论。该论文在算法上，理论上和经验上都对上面的三个设置做出了基本贡献，同时与实际考虑有关。
This thesis rigorously studies fundamental reinforcement learning (RL) methods in modern practical considerations, including robust RL, distributional RL, and offline RL with neural function approximation. The thesis first prepares the readers with an overall overview of RL and key technical background in statistics and optimization. In each of the settings, the thesis motivates the problems to be studied, reviews the current literature, provides computationally efficient algorithms with provable efficiency guarantees, and concludes with future research directions. The thesis makes fundamental contributions to the three settings above, both algorithmically, theoretically, and empirically, while staying relevant to practical considerations.
