Results 241 to 250 of about 139,994 (273)

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

Multi-Agent Cognition Difference Reinforcement Learning for Multi-Agent Cooperation

2021 International Joint Conference on Neural Networks (IJCNN), 2021
Multi-agent cooperation is one of the most attractive research fields in multi-agent systems. There are many attempts made by researchers in this field to promote the cooperation behavior. However, in partially-observable environments, a large number of agents and complex interactions among the agents cause huge difficulty for policy learning. Moreover,
Huimu Wang, Tenghai Qiu, Zhen Liu 0020, Zhiqiang Pu, Jianqiang Yi, Wanmai Yuan   +5 more
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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

Team Policy Learning for Multi-agent Reinforcement Learning

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
This work presents a fully distributed algorithm for learning the optimal policy in a multi-agent cooperative reinforcement learning scenario. We focus on games that can only be solved through coordinated team work. We consider situations in which $K$ players interact simultaneously with an environment and with each other to attain a common goal.
Lucas Cassano, Sulaiman A. Alghunaim, Ali H. Sayed   +2 more
openaire   +1 more source

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

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
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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 ...
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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