Purposive behavior acquisition for a real robot by vision-based reinforcement learning
Machine Learning - Special issue on robot learning
General principles of learning-based multi-agent systems
Proceedings of the third annual conference on Autonomous Agents
Incremental reinforcement learning for designing multi-agent systems
Proceedings of the fifth international conference on Autonomous agents
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement Learning in the Multi-Robot Domain
Autonomous Robots
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Complexity of Decentralized Control of Markov Decision Processes
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Communication in Multi-Agent Markov Decision Processes
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Planning, learning and coordination in multiagent decision processes
TARK '96 Proceedings of the 6th conference on Theoretical aspects of rationality and knowledge
Probabilistic Rewrite Strategies. Applications to ELAN
RTA '02 Proceedings of the 13th International Conference on Rewriting Techniques and Applications
Path Planning for Cooperating Robots Using a GA-Fuzzy Approach
Revised Papers from the International Seminar on Advances in Plan-Based Control of Robotic Agents,
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Task allocation learning in a multiagent environment: Application to the RoboCupRescue simulation
Multiagent and Grid Systems
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A new reinforcement learning (RL) methodology is proposed to design multi-agent systems. In the realistic setting of situated agents with local perception, the task of automatically building a coordinated system is of crucial importance. We use simple reactive agents which learn their own behavior in a decentralized way. To cope with the difficulties inherent to RL used in that framework, we have developed an incremental learning algorithm where agents face more and more complex tasks. We illustrate this general framework on a computer experiment where agents have to coordinate to reach a global goal.