The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Coordinated Reinforcement Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Reinforcement learning of coordination in cooperative multi-agent systems
Eighteenth national conference on Artificial intelligence
Integrating organizational control into multi-agent learning
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
As Safe As It Gets: Near-Optimal Learning in Multi-Stage Games with Imperfect Monitoring
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
| Hi-index | 0.00 |
In comparison to single agent learning, reinforcement learning in a multiagent scenario is more challenging, since there is an increase in the space of combination of actions that may have to be explored before agents learn an efficient policy. Among other approaches, there has been a proposition to address this problem by means of biasing the exploration. We follow this track using an organizational structure where low-level agents mainly use reinforcement learning, while also getting recommendations from agents possessing a broader view. These agents keep a base of cases in order to give such recommendations, orchestrating the process. We show that this approach is able to accelerate and improve learning in penalty games, a especial case of coordination games.