no code implementations • 29 Aug 2023 • Lei Han, Qingxu Zhu, Jiapeng Sheng, Chong Zhang, Tingguang Li, Yizheng Zhang, He Zhang, Yuzhen Liu, Cheng Zhou, Rui Zhao, Jie Li, Yufeng Zhang, Rui Wang, Wanchao Chi, Xiong Li, Yonghui Zhu, Lingzhu Xiang, Xiao Teng, Zhengyou Zhang
In this work, we propose a framework for driving legged robots act like real animals with lifelike agility and strategy in complex environments.
no code implementations • 13 Jun 2022 • Jiawei Xu, Cheng Zhou, Yizheng Zhang, Baoxiang Wang, Lei Han
Integrating the two algorithms results in the complete Relative Policy-Transition Optimization (RPTO) algorithm, in which the policy interacts with the two environments simultaneously, such that data collections from two environments, policy and transition updates are completed in one closed loop to form a principled learning framework for policy transfer.
no code implementations • 29 Sep 2021 • Lei Han, Cheng Zhou, Yizheng Zhang
We propose a new general theory measuring the relativity between two arbitrary Markov Decision Processes (MDPs) from the perspective of reinforcement learning (RL).
no code implementations • 21 Jul 2020 • Jingyi Huang, Yizheng Zhang, Fabio Giardina, Andre Rosendo
While considering Sim and Real learning, our experiments show that the sample-efficient Deep Bayesian RL performance is better than DRL even when computation time (as opposed to number of iterations) is taken in consideration.
no code implementations • 8 Oct 2019 • Yizheng Zhang, Andre Rosendo
Deep Reinforcement Learning (DRL) has shown its promising capabilities to learn optimal policies directly from trial and error.