Technical Note: \cal Q-Learning
Machine Learning
Communication in reactive multiagent robotic systems
Autonomous Robots
On being a teammate: experiences acquired in the design of RoboCup teams
Proceedings of the third annual conference on Autonomous Agents
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
A multiagent reinforcement learning algorithm using extended optimal response
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Friend-or-Foe Q-learning in General-Sum Games
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Reinforcement Learning of Coordination in Heterogeneous Cooperative Multi-Agent Systems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
An Improved Multiagent Reinforcement Learning Algorithm
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
QUICR-learning for multi-agent coordination
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Journal of Artificial Intelligence Research
Iterative Consensus for a Class of Second-order Multi-agent Systems
Journal of Intelligent and Robotic Systems
| Hi-index | 0.00 |
In this paper, we propose a distributed dynamic correlation matrix based multi-Q (D-DCM-Multi-Q) learning method for multi-robot systems. First, a dynamic correlation matrix is proposed for multi-agent reinforcement learning, which not only considers each individual robot's Q-value, but also the correlated Q-values of neighboring robots. Then, the theoretical analysis of the system convergence for this D-DCM-Multi-Q method is provided. Various simulations for multi-robot foraging as well as a proof-of-concept experiment with a physical multi-robot system have been conducted to evaluate the proposed D-DCM-Multi-Q method. The extensive simulation/experimental results show the effectiveness, robustness, and stability of the proposed method.