1 code implementation • 26 Aug 2024 • Xinyang Gu, Yen-Jen Wang, Xiang Zhu, Chengming Shi, Yanjiang Guo, Yichen Liu, Jianyu Chen
In this work, we introduce Denoising World Model Learning (DWL), an end-to-end reinforcement learning framework for humanoid locomotion control, which demonstrates the world's first humanoid robot to master real-world challenging terrains such as snowy and inclined land in the wild, up and down stairs, and extremely uneven terrains.
1 code implementation • 8 Apr 2024 • Xinyang Gu, Yen-Jen Wang, Jianyu Chen
Humanoid-Gym is an easy-to-use reinforcement learning (RL) framework based on Nvidia Isaac Gym, designed to train locomotion skills for humanoid robots, emphasizing zero-shot transfer from simulation to the real-world environment.
no code implementations • 20 Dec 2019 • Jingbin Liu, Shuai Liu, Xinyang Gu
Deep Q Network (DQN) is a very successful algorithm, yet the inherent problem of reinforcement learning, i. e. the exploit-explore balance, remains.
no code implementations • 2 Dec 2019 • Jingbin Liu, Xinyang Gu, Shuai Liu
We introduce a local action variance for policy network and find it can work collaboratively with the idea of entropy regularization.
no code implementations • 30 Aug 2019 • Jingbin Liu, Xinyang Gu, Shuai Liu
We propose an agent framework that integrates off-policy reinforcement learning with world model learning, so as to embody the important features of intelligence in our algorithm design.