Results 251 to 260 of about 10,350,910 (281)

Multi-Agent Reinforcement Learning

2020
In reinforcement learning, complicated applications require involving multiple agents to handle different kinds of tasks simultaneously. However, increasing the number of agents brings in the challenges on managing the interactions among them. In this chapter, according to the optimization problem for each agent, equilibrium concepts are put forward to
Huaqing Zhang, Shanghang Zhang
openaire   +3 more sources

Reinforcement Learning of Communication in a Multi-agent Context

2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2011
In this paper, we present a reinforcement learning approach for multi-agent communication in order to learn what to communicate, when and to whom. This method is based on introspective agents that can reason about their own actions and data so as to construct appropriate communicative acts.
Hoët, Shirley, Sabouret, Nicolas
openaire   +1 more source

Adaptive Learning Rates for Multi-Agent Reinforcement Learning

International Joint Conference on Autonomous Agents and Multiagent Systems, 2023
In multi-agent reinforcement learning (MARL), the learning rates of actors and critic are mostly hand-tuned and fixed. This not only requires heavy tuning but more importantly limits the learning. With adaptive learning rates according to gradient patterns, some optimizers have been proposed for general optimizations, which however do not take into ...
Jiechuan Jiang, Zongqing Lu 0002
openaire   +2 more sources

Explanations for Multi-Agent Reinforcement Learning

Proceedings of the AAAI Conference on Artificial Intelligence
Explainable reinforcement learning (xRL) provides explanations for ``black-box" decision making systems. However, most work in xRL is based on single-agent settings instead of the more complex multi-agent reinforcement learning (MARL). Several different types of post-hoc explanations must be provided to increase understanding of both centralized and ...
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Generalized learning automata for multi-agent reinforcement learning

AI Communications, 2010
A major challenge in multi-agent reinforcement learning remains dealing with the large state spaces typically associated with realistic multi-agent systems. As the state space grows, agent policies become increasingly complex and learning slows down. Currently, advanced single-agent techniques are already very capable of learning optimal policies in ...
Yann-Michaël De Hauwere, Peter Vrancx, Ann Nowé   +2 more
openaire   +3 more sources

Multi-agent Exploration with Reinforcement Learning

2022 30th Mediterranean Conference on Control and Automation (MED), 2022
Alkis Sygkounas, Dimitris Tsipianitis, George Nikolakopoulos, Charalampos P. Bechlioulis   +3 more
openaire   +1 more source

Multi-agent reinforcement learning

2016 13th Symposium on Neural Networks and Applications (NEUREL), 2016
Reinforcement learning deals with the problem of how to map situations (states) to actions so as to maximize a numerical reward while interacting with dynamical and uncertain environment. Within the framework of Markov Decision Processes (MDPs) these methods are typically based on approximate dynamic programming using appropriate calculation ...
openaire   +1 more source

Multi-agent deep reinforcement learning: a survey

Artificial Intelligence Review, 2021
Sven Gronauer
exaly  

Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial

IEEE Communications Surveys and Tutorials, 2021
Ekram Hossain
exaly  

Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey

IEEE Communications Surveys and Tutorials, 2022
Kun Zhu, Qihui Wu, Yang Zhang
exaly