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
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Reinforcement Learning
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Experimental Results on Q-Learning for General-Sum Stochastic Games
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Convergence Problems of General-Sum Multiagent Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Rational and convergent learning in stochastic games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Value-function reinforcement learning in Markov games
Cognitive Systems Research
Nash q-learning for general-sum stochastic games
The Journal of Machine Learning Research
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Bounded rationality via recursion
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Intelligent negotiation behaviour model for an open railway access market
Expert Systems with Applications: An International Journal
Distributed Reinforcement Learning for Coordinate Multi-Robot Foraging
Journal of Intelligent and Robotic Systems
Opponent learning for multi-agent system simulation
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
An overview of cooperative and competitive multiagent learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Stochastic games provides a theoretical framework to multiagent reinforcement learning. Based on the framework, a multiagent reinforcement learning algorithm for zero-sum stochastic games was proposed by Littman and it was extended to general-sum games by Hu and Wellman. Given a stochastic game, if all agents learn with their algorithm, we can expect that the policies of the agents converge to a Nash equilibrium. However, agents with their algorithm always try to converge to a Nash equilibrium independent of the policies used by the other agents. In addition, in case there are multiple Nash equilibria, agents must agree on the equilibrium where they want to reach. Thus, their algorithm lacks adaptability in a sense. In this paper, we propose a multiagent reinforcement learning algorithm. The algorithm uses the extended optimal response which we introduce in this paper. It will converge to a Nash equilibrium when other agents are adaptable, otherwise it will make an optimal response. We also provide some empirical results in three simple stochastic games, which show that the algorithm can realize what we intend.