Feature-based methods for large scale dynamic programming
Machine Learning - Special issue on reinforcement learning
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
Nash q-learning for general-sum stochastic games
The Journal of Machine Learning Research
Modeling dynamic environments in multi-agent simulation
Autonomous Agents and Multi-Agent Systems
Self-healing systems - survey and synthesis
Decision Support Systems
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Coordinated learning in multiagent MDPs with infinite state-space
Autonomous Agents and Multi-Agent Systems
Social welfare for automatic innovation
MATES'11 Proceedings of the 9th German conference on Multiagent system technologies
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A dynamic environment whose behavior may change in time presents a challenge that agents located there will have to solve. Changes in an environment e.g. a market, can be quite drastic: from changing the dependencies of some products to add new actions to build new products. The agents working in this environment would have to be ready to embrace this changes to improve their performance which otherwise would be diminished. Also, they should try to cooperate or compete against others, when appropriated, to reach their goals faster than in an individual fashion, showing an always desirable emergent behavior. In this paper a reinforcement learning method proposal, guided by social interaction between agents, is presented. The proposal aims to show that adaptation is performed independently by the society, without explicitly reporting that changes have occurred by a central authority, or even by trying to recognize those changes.