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
Planning, learning and coordination in multiagent decision processes
TARK '96 Proceedings of the 6th conference on Theoretical aspects of rationality and knowledge
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One important class of problems in Multi-Agent Systems (MASs) is planning, that is constructing an optimal policy for each agent with the objective of reaching some terminal goal state. The key aspect of multi-agent planning is coordinating the actions of the individual agents. This coordination may be done through communication, learning, or conventions imposed at design time. In this paper we present a new taxonomy of MASs that is based on the notions of optimality and rationality. A framework that describes the interactions between the agents and their environment is given, along with a reinforcement learning-based algorithm (Q-learning) for learning a joint optimal plan. Finally, we give some experimental results on grid games that show the convergence of this algorithm.