Incremental multi-step Q-learning
Machine Learning - Special issue on reinforcement learning
Machine Learning
A unified analysis of value-function-based reinforcement learning algorithms
Neural Computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
On Multiagent Q-Learning in a Semi-Competitive Domain
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
An Introduction to Linear Programming and Game Theory
An Introduction to Linear Programming and Game Theory
Experience generalization for concurrent reinforcement learners: the minimax-QS algorithm
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Convergent Gradient Ascent in General-Sum Games
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Adaptive policy gradient in multiagent learning
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Nash q-learning for general-sum stochastic games
The Journal of Machine Learning Research
Theory of moves learners: towards non-myopic equilibria
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Perspectives on multiagent learning
Artificial Intelligence
Reactivity and Safe Learning in Multi-Agent Systems
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Heuristic selection of actions in multiagent reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Case-Based Multiagent Reinforcement Learning: Cases as Heuristics for Selection of Actions
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
The success and failure of tag-mediated evolution of cooperation
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
Multiagent learning in the presence of memory-bounded agents
Autonomous Agents and Multi-Agent Systems
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When several agents learn concurrently, the payoff received by an agent is dependent on the behavior of the other agents. As the other agents learn, the reward of one agent becomes non-stationary. This makes learning in multiagent systemsmore difficult than single-agent learning. A few methods, how-ever, are known to guarantee convergence to equilibrium in the limit in such systems. In this paper we experimentally study one such technique, the minimax-Q, in a competitive domain and prove its equivalence with another well-known method for competitive domains. We study the rate of convergence of minimax-Q and investigate possible ways for increasing the same. We also present a variant of the algorithm, minimax-SARSA, and prove its convergence to minimax-Q values under appropriate conditions. Finally we show that this new algorithm performs better than simple minimax-Q in a general-sum domain as well.