Learning to coordinate without sharing information
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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
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Nash q-learning for general-sum stochastic games
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ICML '04 Proceedings of the twenty-first 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
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Graph-Based Domain Mapping for Transfer Learning in General Games
ECML '07 Proceedings of the 18th European conference on Machine Learning
Learning of coordination: exploiting sparse interactions in multiagent systems
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Autonomous inter-task transfer in reinforcement learning domains
Autonomous inter-task transfer in reinforcement learning domains
Reinforcement learning: a survey
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General game learning using knowledge transfer
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Transfer Learning for Reinforcement Learning Domains: A Survey
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Learning multi-agent state space representations
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
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A major challenge in multi-agent reinforcement learning remains dealing with the large state spaces typically associated with realistic multi-agent systems. As the state space grows, agent policies become increasingly complex and learning slows down. Currently, advanced single-agent techniques are already very capable of learning optimal policies in large unknown environments. When multiple agents are present however, we are challenged by an increase of the state-action space, exponential in the number of agents, even though these agents do not always interfere with each other and thus their presence should not always be included in the state information of the other agent. A solution to this problem lies in the use of generalized learning automata (GLA). In this paper we will first demonstrate how GLA can help take the correct actions in large unknown multi-agent environments. Furthermore we introduce a framework capable of dealing with this issue of observing other agents. We also present an implementation of our framework, called 2observe which we apply to some gridworld problems. Finally, we demonstrate that our approach is capable of transferring its knowledge to new agents entering the environment.