主要研究方向
王若松的主要研究领域是机器学习理论，包括对强化学习和深度学习理论基础的研究。他目前的主要研究方向为：（1）设计有理论保证的强化学习算法，（2）证明强化学习问题的采样复杂度下界，（3）在理论研究的基础上，设计更高效、更鲁棒的强化学习系统和更合理的强化学习算法评估框架。
Selected Publications
R. Wang, D. P. Foster, and S. M. Kakade. What are the statistical limits of offline RL with linear function approximation? In ICLR, 2021.
R. Wang, R. Salakhutdinov, and L. F. Yang. Reinforcement learning with general value function approximation: Provably efficient approach via bounded eluder dimension. In NeurIPS, 2020.
R. Wang, S. S. Du, L. F. Yang, and S. M. Kakade. Is long horizon RL more difficult than short horizon RL? In NeurIPS, 2020.
S. Arora, S. S. Du, W. Hu, Z. Li, R. Salakhutdinov, and R. Wang. On exact computation with an infinitely wide neural net. In NeurIPS, 2019.
