K Zhang, Z Yang, T Başar - Handbook of reinforcement learning and …, 2021 - Springer
… reinforcementlearning (RL), which has registered tremendous success in solving various sequential decision-making problems in machine learning… the realm of multi-agent RL (MARL), …
M Tan - … the tenth international conference on machine learning, 1993 - books.google.com
… case studies of multiagentreinforcementlearning involving such cooperation and draws some related conclusions that are not limited to multi-agentreinforcementlearning. The main …
… and challenges of multi-agentreinforcementlearning are … a multi-agentlearning goal; this chapter reviews the learning goals … where multi-agentreinforcementlearning techniques have …
Y Yang, J Wang - arXiv preprint arXiv:2011.00583, 2020 - arxiv.org
… advances in multi-agentreinforcementlearning (MARL) techniques. MARL corresponds to the learning problem in a multi-agent system in which multiple agents learn simultaneously. It …
L Busoniu, R Babuska… - 2006 9th International …, 2006 - ieeexplore.ieee.org
… We have reviewed the challenges of multi-agentreinforcement learning, the methods to address them, and we have provided specific conclusions and open issues for each class of …
… Our approach is programmatic: first, we propose a set of multi-agent benchmark tasks … learning algorithms for these tasks; finally, we analyse how these algorithms learn, or fail to learn, …
ML Littman - Machine learning proceedings 1994, 1994 - Elsevier
… about multi-agent environments. In particular, the paper describes a reinforcementlearning … in the update step of a standard Q-learning algorithm is replaced by a "minimax" operator …
… other learners or by competing with them. This chapter focuses on the application reinforcement learning techniques in multi-agent systems. We describe a basic learning framework …