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
Relational reinforcement learning
Machine Learning - Special issue on inducive logic programming
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Social Agents Playing a Periodical Policy
EMCL '01 Proceedings of the 12th European 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
Collective Intelligence and Braess' Paradox
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A Framework for Behavioural Cloning
Machine Intelligence 15, Intelligent Agents [St. Catherine's College, Oxford, July 1995]
Dispersion games: general definitions and some specific learning results
Eighteenth national conference on Artificial intelligence
Towards a pareto-optimal solution in general-sum games
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A selection-mutation model for q-learning in multi-agent systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Learning to fly by combining reinforcement learning with behavioural cloning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Integrating Guidance into Relational Reinforcement Learning
Machine Learning
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Multi-agent relational reinforcement learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
An overview of cooperative and competitive multiagent learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
Multi-agent relational reinforcement learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
Distributed relational temporal difference learning
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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In this paper we report on using a relational state space in multi-agent reinforcement learning. There is growing evidence in the Reinforcement Learning research community that a relational representation of the state space has many benefits over a propositional one. Complex tasks as planning or information retrieval on the web can be represented more naturally in relational form. Yet, this relational structure has not been exploited for multi-agent reinforcement learning tasks and has only been studied in a single agent context so far. In this paper we explore the powerful possibilities of using Relational Reinforcement Learning (RRL) in complex multi-agent coordination tasks. More precisely, we consider an abstract multi-state coordination problem, which can be considered as a variation and extension of repeated stateless Dispersion Games. Our approach shows that RRL allows to represent a complex state space in a multi-agent environment more compactly and allows for fast convergence of learning agents. Moreover, with this technique, agents are able to make complex interactive models (in the sense of learning from an expert), to predict what other agents will do and generalize over this model. This enables to solve complex multi-agent planning tasks, in which agents need to be adaptive and learn, with more powerful tools.