Competitive Markov decision processes
Competitive Markov decision processes
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
Adaptivity in agent-based routing for data networks
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
A unified analysis of value-function-based reinforcement learning algorithms
Neural Computation
Using collective intelligence to route Internet traffic
Proceedings of the 1998 conference on Advances in neural information processing systems II
Gradient descent for general reinforcement learning
Proceedings of the 1998 conference on Advances in neural information processing systems II
Multiagent learning using a variable learning rate
Artificial Intelligence
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Friend-or-Foe Q-learning in General-Sum Games
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Learning to Reach the Pareto Optimal Nash Equilibrium as a Team
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Learning to Cooperate via Policy Search
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Nash Convergence of Gradient Dynamics in General-Sum Games
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Reinforcement learning of coordination in cooperative multi-agent systems
Eighteenth national conference on Artificial intelligence
Asymmetric Multiagent Reinforcement Learning
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Complexity results about Nash equilibria
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Acquisition of a concession strategy in multi-issue negotiation
Web Intelligence and Agent Systems
| Hi-index | 0.00 |
A novel model for asymmetric multiagent reinforcement learning is introduced in this paper. The model addresses the problem where the information states of the agents involved in the learning task are not equal; some agents (leaders) have information how their opponents (followers) will select their actions and based on this information leaders encourage followers to select actions that lead to improved payoffs for the leaders. This kind of configuration arises e.g. in semi-centralized multiagent systems with an external global utility associated to the system. We present a brief literature survey of multiagent reinforcement learning based on Markov games and then propose an asymmetric learning model that utilizes the theory of Markov games. Additionally, we construct a practical learning method based on the proposed learning model and study its convergence properties. Finally, we test our model with a simple example problem and a larger two-layer pricing application.