Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The MAXQ Method for Hierarchical Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Multi-Agent Reinforcement Learning for Planning and Scheduling Multiple Goals
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Learning to Communicate and Act Using Hierarchical Reinforcement Learning
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Multiagent learning in the presence of agents with limitations
Multiagent learning in the presence of agents with limitations
Experience-based reinforcement learning to acquire effective behavior in a multi-agent domain
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
An overview of cooperative and competitive multiagent learning
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
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This paper presents some methods of dealing with the problem of cooperative learning in a multi-agent system, in error prone environments. A system is developed that learns by reinforcement and is robust to errors that can come from the agents' sensors, from another agent that shares wrong information or even from the communication channel.