no code implementations • 21 Jan 2024 • Ge Li, Hongyi Zhou, Dominik Roth, Serge Thilges, Fabian Otto, Rudolf Lioutikov, Gerhard Neumann
Current advancements in reinforcement learning (RL) have predominantly focused on learning step-based policies that generate actions for each perceived state.
no code implementations • 22 Jun 2023 • Fabian Otto, Hongyi Zhou, Onur Celik, Ge Li, Rudolf Lioutikov, Gerhard Neumann
We introduce a novel deep reinforcement learning (RL) approach called Movement Primitive-based Planning Policy (MP3).
1 code implementation • 18 Oct 2022 • Fabian Otto, Onur Celik, Hongyi Zhou, Hanna Ziesche, Ngo Anh Vien, Gerhard Neumann
In this paper, we present a new algorithm for deep ERL.
1 code implementation • 15 Oct 2020 • Zuxin Liu, Hongyi Zhou, Baiming Chen, Sicheng Zhong, Martial Hebert, Ding Zhao
We propose a model-based approach to enable RL agents to effectively explore the environment with unknown system dynamics and environment constraints given a significantly small number of violation budgets.
Model-based Reinforcement Learning Model Predictive Control +3
no code implementations • 30 Jul 2020 • Zuxin Liu, Baiming Chen, Hongyi Zhou, Guru Koushik, Martial Hebert, Ding Zhao
Multi-agent navigation in dynamic environments is of great industrial value when deploying a large scale fleet of robot to real-world applications.