Purposive behavior acquisition for a real robot by vision-based reinforcement learning
Machine Learning - Special issue on robot learning
Online learning about other agents in a dynamic multiagent system
AGENTS '98 Proceedings of the second international conference on Autonomous agents
General principles of learning-based multi-agent systems
Proceedings of the third annual conference on Autonomous Agents
Team-partitioned, opaque-transition reinforcement learning
Proceedings of the third annual conference on Autonomous Agents
Learning Team Strategies: Soccer Case Studies
Machine Learning
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Reinforcement Learning in the Multi-Robot Domain
Autonomous Robots
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Discovering Hierarchy in Reinforcement Learning with HEXQ
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Shaping in Reinforcement Learning by Changing the Physics of the Problem
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Planning, Learning and Coordination in Multiagent Decision Processes
Proceedings of the Sixth Conference on Theoretical Aspects of Rationality and Knowledge
Opponent Modeling in Multi-Agent Systems
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
Learning to Cooperate via Policy Search
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Collective Intelligence and Braess' Paradox
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Learning to Drive a Bicycle Using Reinforcement Learning and Shaping
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Communication in Multi-Agent Markov Decision Processes
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Exact and approximate algorithms for partially observable markov decision processes
Exact and approximate algorithms for partially observable markov decision processes
Decentralized Language Learning through Acting
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Interactive POMDPs: Properties and Preliminary Results
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Theory and application of reward shaping in reinforcement learning
Theory and application of reward shaping in reinforcement learning
The communicative multiagent team decision problem: analyzing teamwork theories and models
Journal of Artificial Intelligence Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Infinite-horizon policy-gradient estimation
Journal of Artificial Intelligence Research
Experiments with infinite-horizon, policy-gradient estimation
Journal of Artificial Intelligence Research
ECML'05 Proceedings of the 16th European conference on Machine Learning
Neurocomputing
Recent Advances in Reinforcement Learning
An investigation into mathematical programming for finite horizon decentralized POMDPs
Journal of Artificial Intelligence Research
Multi-goal Q-learning of cooperative teams
Expert Systems with Applications: An International Journal
Unified inter and intra options learning using policy gradient methods
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Adaptive learning algorithm of self-organizing teams
Expert Systems with Applications: An International Journal
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An original reinforcement learning (RL) methodology is proposed for the design of 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. To that end, we design simple reactive agents in a decentralized way as independent learners. But to cope with the difficulties inherent to RL used in that framework, we have developed an incremental learning algorithm where agents face a sequence of progressively more complex tasks. We illustrate this general framework by computer experiments where agents have to coordinate to reach a global goal.