Shengyi Huang

According to our database, Shengyi Huang authored at least 15 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarization.

Shengyi Huang, Michael Noukhovitch, Arian Hosseini, Kashif Rasul, Weixun Wang, Lewis Tunstall

CoRR, 2024

Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning.

Shengyi Huang, Quentin Gallouédec, Florian Felten, Antonin Raffin, Rousslan Fernand Julien Dossa, Yanxiao Zhao, Ryan Sullivan, Viktor Makoviychuk, Denys Makoviichuk, Mohamad H. Danesh, Cyril Roumégous, Jiayi Weng, Chufan Chen, Md Masudur Rahman, João G. M. Araújo, Guorui Quan, Daniel Tan, Timo Klein, Rujikorn Charakorn, Mark Towers, Yann Berthelot, Kinal Mehta, Dipam Chakraborty, Arjun KG, Valentin Charraut, Chang Ye, Zichen Liu, Lucas N. Alegre, Alexander Nikulin, Xiao Hu, Tianlin Liu, Jongwook Choi, Brent Yi

CoRR, 2024

2023

Zephyr: Direct Distillation of LM Alignment.

Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, Nathan Sarrazin, Omar Sanseviero, Alexander M. Rush, Thomas Wolf

CoRR, 2023

Cleanba: A Reproducible and Efficient Distributed Reinforcement Learning Platform.

Shengyi Huang, Jiayi Weng, Rujikorn Charakorn, Min Lin, Zhongwen Xu, Santiago Ontañón

CoRR, 2023

Reward Scale Robustness for Proximal Policy Optimization via DreamerV3 Tricks.

Ryan Sullivan, Akarsh Kumar, Shengyi Huang, John P. Dickerson, Joseph Suarez

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms.

Shengyi Huang, Rousslan Fernand Julien Dossa, Chang Ye, Jeff Braga, Dipam Chakraborty, Kinal Mehta, João G. M. Araújo

J. Mach. Learn. Res., 2022

A2C is a special case of PPO.

Shengyi Huang, Anssi Kanervisto, Antonin Raffin, Weixun Wang, Santiago Ontañón, Rousslan Fernand Julien Dossa

CoRR, 2022

EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine.

Jiayi Weng, Min Lin, Shengyi Huang, Bo Liu, Denys Makoviichuk, Viktor Makoviychuk, Zichen Liu, Yufan Song, Ting Luo, Yukun Jiang, Zhongwen Xu, Shuicheng Yan

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A Closer Look at Invalid Action Masking in Policy Gradient Algorithms.

Shengyi Huang, Santiago Ontañón

Proceedings of the Thirty-Fifth International Florida Artificial Intelligence Research Society Conference, 2022

2021

CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms.

Shengyi Huang, Rousslan Fernand Julien Dossa, Chang Ye, Jeff Braga

CoRR, 2021

An Empirical Investigation of Early Stopping Optimizations in Proximal Policy Optimization.

Rousslan Fernand Julien Dossa, Shengyi Huang, Santiago Ontañón, Takashi Matsubara

IEEE Access, 2021

Gym-µRTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning.

Shengyi Huang, Santiago Ontañón, Chris Bamford, Lukasz Grela

Proceedings of the 2021 IEEE Conference on Games (CoG), 2021

2020

Griddly: A platform for AI research in games.

Chris Bamford, Shengyi Huang, Simon M. Lucas

CoRR, 2020

Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games.

Shengyi Huang, Santiago Ontañón

CoRR, 2020

2019

Comparing Observation and Action Representations for Deep Reinforcement Learning in MicroRTS.

Shengyi Huang, Santiago Ontañón

CoRR, 2019