In the field of space exploration, floating platforms play a crucial role in scientific investigations and technological advancements. However, controlling these platforms in zero-gravity environments presents unique challenges, including uncertainties and disturbances. This paper introduces an innovative approach that combines Proximal Policy Optimization (PPO) with Model Predictive Control (MPC) in the zero-gravity laboratory (Zero-G Lab) at the University of Luxembourg. This approach leverages PPO's reinforcement learning power and MPC's precision to navigate the complex control dynamics of floating platforms. Unlike traditional control methods, this PPO-MPC approach learns from MPC predictions, adapting to unmodeled dynamics and disturbances, resulting in a resilient control framework tailored to the zero-gravity environment. Simulations and experiments in the Zero-G Lab validate this approach, showcasing the adaptability of the PPO agent. This research opens new possibilities for controlling floating platforms in zero-gravity settings, promising advancements in space exploration.
PPO-UE: Proximal Policy Optimization via Uncertainty-Aware Exploration
Virginia Tech（弗吉尼亚理工大学）；University of Oslo（奥斯陆大学）；DEVCOM Army Research Laboratory（DEVCOM陆军研究实验室）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；University of the District of Columbia（哥伦比亚特区大学）
Guidance Design for Escape Flight Vehicle Using Evolution Strategy Enhanced Deep Reinforcement Learning
School of Aerospace Engineering, Tsinghua University（清华大学航天航空学院）；China Academy of Launch Vehicle Technology（中国运载火箭技术研究院）；School of Information and Communication Engineering, Beijing University of Posts and Telecommunications（北京邮电大学信息与通信工程学院）
Advantage-level Aggregation Reinforcement Learning for X-point Target Magnetic Configuration Control in an EXL-50U Experiment-Calibrated Simulation Environment
Beijing ENN Fusion Energy Science and Technology Co., Ltd.（北京新奥融合能源科技有限公司）；Beijing Key Laboratory of High Magnetic Field Spherical Torus Fusion Energy（北京强磁场球形托卡马克聚变能源重点实验室）；Hebei Key Laboratory of Compact Fusion（河北省紧凑型聚变重点实验室）；Dalian University of Technology（大连理工大学）；School of Physics, Dalian University of Technology（大连理工大学物理学院）；Key Laboratory of Materials Modification by Beams of the Ministry of Education（教育部材料表面与界面工程重点实验室）；School of Nuclear Science and Engineering, East China University of Technology（东华理工大学核科学与工程学院）；East China University of Technology（东华理工大学）；School of Mechanics and Engineering Science, Peking University（北京大学力学与工程科学学院）；Peking University（北京大学）
A Reinforcement Learning Method for Environments with Stochastic Variables: Post-Decision Proximal Policy Optimization with Dual Critic Networks
Division of Computer Science Instituto Tecnológico de Aeronáutica（计算机科学系 航空航天技术研究院）；Department of Mathematical Sciences Politecnico di Torino（数学科学系 波尔蒂纳罗内理工大学）；Escola Politécnica Universidade de São Paulo（工学院 圣保罗大学）；Instituto Tecnológico de Aeronáutica（航空航天技术研究院）
Trajectory Planning for Teleoperated Space Manipulators Using Deep Reinforcement Learning
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Research Institute of Tsinghua University in Shenzhen（清华大学深圳研究院）；Department of Automation, Tsinghua University（清华大学自动化系）
Resolving Action Bottleneck: Agentic Reinforcement Learning Informed by Token-Level Energy
University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Technical University of Berlin（柏林技术大学）；University of Southern California（南加州大学）；University of Hong Kong（香港大学）；Broad Institute of MIT and Harvard（MIT和哈佛大学Broad研究所）
AGPO: Adaptive Group Policy Optimization with Dual Statistical Feedback
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Maximizing Seaweed Growth on Autonomous Farms: A Dynamic Programming Approach for Underactuated Systems Navigating on Uncertain Ocean Currents
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley（加州大学伯克利分校电气工程与计算机科学系）；School of Computation, Information and Technology of the Technical University of Munich（慕尼黑技术大学计算、信息与技术学院）；Department of Mechanical Engineering at the Massachusetts Institute of Technology（麻省理工学院机械工程系）
UAV-Supported Maritime Search System: Experience from Valun Bay Field Trials
Faculty of Engineering, University of Rijeka（里耶卡大学工程学院）；Center for Advanced Computing and Modeling, University of Rijeka（里耶卡大学高级计算与建模中心）；Faculty of Mathematics, University of Rijeka（里耶卡大学数学学院）
