Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
- Gen Li,
Gen Li
[email protected]Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104;
- Yuting Wei ,
Corresponding Author
Yuting Wei
[email protected]https://orcid.org/0000-0003-1488-4647
Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104;
- Yuejie Chi ,
Yuejie Chi
[email protected]https://orcid.org/0000-0002-6766-5459
Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213;
- Yuxin Chen
Yuxin Chen
[email protected]https://orcid.org/0000-0001-9256-5815
Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104;Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Gen Li
[email protected]Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104;
Corresponding Author
Yuting Wei
[email protected]https://orcid.org/0000-0003-1488-4647
Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104;
Yuejie Chi
[email protected]https://orcid.org/0000-0002-6766-5459
Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213;
Yuxin Chen
[email protected]https://orcid.org/0000-0001-9256-5815
Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104;Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104
