Multi-agent reinforcement learning for redundant robot control in task-space

2020 ◽  
Author(s):  
Adolfo Perrusquía ◽  
Wen Yu ◽  
Xiaoou Li
Keyword(s):  
Reinforcement Learning ◽  
Robot Control ◽  
Task Space ◽  
Redundant Robot ◽  
Multi Agent

scholarly journals Cooperative Multi-Agent Reinforcement Learning for Multi-Component Robotic Systems: guidelines for future research

2011 ◽  
Vol 2 (2) ◽  
Author(s):  
Manuel Graña ◽  
Borja Fernandez-Gauna ◽  
Jose Manuel Lopez-Guede
Keyword(s):  
Reinforcement Learning ◽  
Control Systems ◽  
Robot Control ◽  
Robotic Systems ◽  
Future Research ◽  
Multi Agent Systems ◽  
Feedback Information ◽  
Agent Systems ◽  
Innovative Solutions ◽  
Multi Agent

AbstractReinforcement Learning (RL) as a paradigm aims to develop algorithms that allow to train an agent to optimally achieve a goal with minimal feedback information about the desired behavior, which is not precisely specified. Scalar rewards are returned to the agent as response to its actions endorsing or opposing them. RL algorithms have been successfully applied to robot control design. The extension of the RL paradigm to cope with the design of control systems for Multi-Component Robotic Systems (MCRS) poses new challenges, mainly related to coping with scaling up of complexity due to the exponential state space growth, coordination issues, and the propagation of rewards among agents. In this paper, we identify the main issues which offer opportunities to develop innovative solutions towards fully-scalable cooperative multi-agent systems.

Multi-agent deep reinforcement learning with type-based hierarchical group communication

2021 ◽  
Author(s):  
Hao Jiang ◽  
Dianxi Shi ◽  
Chao Xue ◽  
Yajie Wang ◽  
Gongju Wang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Group Communication ◽  
Multi Agent ◽  
Hierarchical Group

Multi-Agent Deep Reinforcement Learning for Vehicular Computation Offloading in IoT

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Xiaoyu Zhu ◽  
Yueyi Luo ◽  
Anfeng Liu ◽  
Md Zakirul Alam Bhuiyan ◽  
Shaobo Zhang
Keyword(s):  
Reinforcement Learning ◽  
Computation Offloading ◽  
Multi Agent

scholarly journals Multi-Agent Reinforcement Learning: A Review of Challenges and Applications

2021 ◽  
Vol 11 (11) ◽  
pp. 4948
Author(s):  
Lorenzo Canese ◽  
Gian Carlo Cardarilli ◽  
Luca Di Di Nunzio ◽  
Rocco Fazzolari ◽  
Daniele Giardino ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Mathematical Models ◽  
Learning Algorithms ◽  
Single Agent ◽  
Critical Issues ◽  
Multi Agent ◽  
Pros And Cons ◽  
Application Fields

In this review, we present an analysis of the most used multi-agent reinforcement learning algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the most critical issues that must be taken into account in their extension to multi-agent scenarios. The analyzed algorithms were grouped according to their features. We present a detailed taxonomy of the main multi-agent approaches proposed in the literature, focusing on their related mathematical models. For each algorithm, we describe the possible application fields, while pointing out its pros and cons. The described multi-agent algorithms are compared in terms of the most important characteristics for multi-agent reinforcement learning applications—namely, nonstationarity, scalability, and observability. We also describe the most common benchmark environments used to evaluate the performances of the considered methods.

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