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
Coordinated Reinforcement Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Collaborative Multiagent Reinforcement Learning by Payoff Propagation
The Journal of Machine Learning Research
Opportunities for multiagent systems and multiagent reinforcement learning in traffic control
Autonomous Agents and Multi-Agent Systems
Improving action selection in MDP's via knowledge transfer
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
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Reinforcement learning is an efficient, widely used machine learning technique that performs well in problems that are characterized by a small number of states and actions. This is rarely the case in multiagent learning problems. For the multiagent case, standard approaches may not be adequate. As an alternative, it is possible to use techniques that generalize the state space to allow agents to learn through the use of abstractions. Thus, the focus of this work is to combine multiagent learning with a generalization technique, namely tile coding. This kind of method is key in scenarios where agents have a high number of states to explore. In the scenarios used to test and validate this approach, our results indicate that the proposed representation outperforms the tabular one and is then an effective alternative.