Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Exploration Strategies for Model-based Learning in Multi-agent Systems: Exploration Strategies
Autonomous Agents and Multi-Agent Systems
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Classifier fitness based on accuracy
Evolutionary Computation
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
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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Learning agents have to deal with the exploration-exploitation dilemma. The choice between exploration and exploitation is very difficult in dynamic systems; in particular in large scale ones such as economic systems. Recent research shows that there is neither an optimal nor a unique solution for this problem. In this paper, we propose an adaptive approach based on meta-rules to adapt the choice between exploration and exploitation. This new adaptive approach relies on the variations of the performance of the agents. To validate the approach, we apply it to economic systems and compare it to two adaptive methods originally proposed by Wilson: one local and one global. Moreover, we compare different exploration strategies and focus on their influence on the performance of the agents.