no code implementations • 15 Feb 2024 • Quentin Gallouédec, Edward Beeching, Clément Romac, Emmanuel Dellandréa
The search for a general model that can operate seamlessly across multiple domains remains a key goal in machine learning research.
1 code implementation • 5 Feb 2024 • 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
As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone.
1 code implementation • 31 Aug 2022 • Quentin Gallouédec, Emmanuel Dellandréa
In this paper, we introduce Latent Go-Explore (LGE), a simple and general approach based on the Go-Explore paradigm for exploration in reinforcement learning (RL).
1 code implementation • 25 Jun 2021 • Quentin Gallouédec, Nicolas Cazin, Emmanuel Dellandréa, Liming Chen
This technical report presents panda-gym, a set Reinforcement Learning (RL) environments for the Franka Emika Panda robot integrated with OpenAI Gym.