Reinforcement learning (RL) has been successfully used in various simulations and computer games. Industry-related applications, such as autonomous mobile robot motion control, are somewhat challenging for RL up to date though. This paper presents an experimental evaluation of predictive RL controllers for optimal mobile robot motion control. As a baseline for comparison, model-predictive control (MPC) is used. Two RL methods are tested: a roll-out Q-learning, which may be considered as MPC with terminal cost being a Q-function approximation, and a so-called stacked Q-learning, which in turn is like MPC with the running cost substituted for a Q-function approximation. The experimental foundation is a mobile robot with a differential drive (Robotis Turtlebot3). Experimental results showed that both RL methods beat the baseline in terms of the accumulated cost, whereas the stacked variant performed best. Provided the series of previous works on stacked Q-learning, this particular study supports the idea that MPC with a running cost adaptation inspired by Q-learning possesses potential of performance boost while retaining the nice properties of MPC.
Cost-Matching Model Predictive Control for Efficient Reinforcement Learning in Humanoid Locomotion
New York University Abu Dhabi (NYUAD)（纽约大学阿布扎比分校）；Georgia Institute of Technology（佐治亚理工学院）；Norwegian University of Science and Technology (NTNU)（挪威科技大学）
Chance-Aware Lane Change with High-Level Model Predictive Control Through Curriculum Reinforcement Learning
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；The Hong Kong University of Science and Technology（香港科技大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深港协同创新研究院）
Safe Reinforcement Learning with a Predictive Safety Filter for Motion Planning and Control: A Drifting Vehicle Example
State Key Laboratory of Industrial Control Technology, Zhejiang University（浙江大学工业控制技术国家重点实验室）；Professorship of Autonomous Vehicle Systems, Technical University of Munich（慕尼黑工业大学自主车辆系统教授职位）
Residual MPC: Blending Reinforcement Learning with GPU-Parallelized Model Predictive Control
Department of Mechanical Engineering, Massachusetts Institute of Technology（麻省理工学院机械工程系）；Department of Mechanical Engineering, School of Smart Mobility, Korea University（韩国大学智能移动学院机械工程系）
Reinforcement Learning Compensated Model Predictive Control for Off-road Driving on Unknown Deformable Terrain
Department of Automotive Engineering, Clemson University（汽车工程系，克莱姆森大学）；Ground Vehicle Systems Center, U.S. Army Combat Capabilities Development Command（地面车辆系统中心，美国陆军作战能力发展指挥部）
CARE: Consistent Action-Response Execution for Human-Guided Robot Transport
School of Engineering, The University of Western Australia（西澳大学工程学院）；Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong（香港中文大学机械与自动化工程系）；School of Electrical Engineering and Computer Science, The University of Queensland（昆士兰大学电气工程与计算机科学学院）
SRL-MPC: Shape-Aware Reinforcement Learned Model Predictive Control
The University of Hong Kong（香港大学）；Southern University of Science and Technology（南方科技大学）；University of Michigan（密歇根大学）；Shenzhen Institutes of Advanced Technology（深圳先进技术研究院）
A Systematic Review and Taxonomy of Reinforcement Learning-Model Predictive Control Integration for Linear Systems
Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano（电子信息生物工程系（DEIB），米兰理工大学）；Department of Applied Mathematics, Ferdowsi University of Mashhad（应用数学系，法尔德大学）；Department of Mechanical Engineering, Politecnico di Milano（机械工程系，米兰理工大学）；Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan（应用数学系，加林大学）；Department of Robotics and Control Engineering, Shahrood University of Technology（机器人与控制工程系，沙霍德技术大学）
A Safe Reinforcement Learning driven Weights-varying Model Predictive Control for Autonomous Vehicle Motion Control
Technical University Munich（慕尼黑工业大学）；TUM School of Engineering and Design（慕尼黑工业大学工程与设计学院）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）
Predictive Safety Shield for Dyna-Q Reinforcement Learning
Departement U2IS, ENSTA Paris, Institut Polytechnique de Paris（ENSTA巴黎大学U2IS部门，巴黎理工学院）；Department of Electrical Engineering and Computer Science, University of California, Berkeley, USA（加州大学伯克利分校电气工程与计算机科学系）
Decentralized End-to-End Multi-AAV Pursuit Using Predictive Spatio-Temporal Observation via Deep Reinforcement Learning
School of Intelligence Science and Engineering（智能科学与工程学院）；Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics（广东省智能变形机制与自适应机器人重点实验室）；Shenzhen Key Lab for Advanced Motion Control and Modern Automation Equipments（深圳先进运动控制与现代自动化设备重点实验室）；Harbin Institute of Technology（哈尔滨工业大学）
Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamics
GRASP Lab, University of Pennsylvania, USA（宾夕法尼亚大学GRASP实验室）；Department of Computer and Information Science, University of Pennsylvania, USA（宾夕法尼亚大学计算机与信息科学系（注：根据机构粒度要求，这里提取到大学级别，即“宾夕法尼亚大学”，但原文明确写了具体部门，所以保留原文部门名+大学的组合））
Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving
Robert Bosch Centre for Cyber-Physical Systems, Indian Institute of Science（印度科学学院罗伯特·博世信息物理系统中心）；Indian Institute of Science（印度科学学院）；PES university（PES大学）
