Abstract
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.
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García-Pardo, J.A., Soler, J., Carrascosa, C. (2010). Social Conformity and Its Convergence for Reinforcement Learning. In: Dix, J., Witteveen, C. (eds) Multiagent System Technologies. MATES 2010. Lecture Notes in Computer Science(), vol 6251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16178-0_15
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DOI: https://doi.org/10.1007/978-3-642-16178-0_15
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