Improvement of Agents Performance in Artificial Society Using Reinforcement Learning

Subject Areas : Social Researches

Amirpooyan Khodabakhshi ¹ , Arash Rahman ² , Mohsen Rohani ³

1 - گروه مهندسی کامپیوتر، دانشکده فنی مهندسی، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران[e-mail]
2 - گروه مهندسی کامپیوتر، دانشکده فنی مهندسی، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران[e-mail]
3 - گروه مهندسی کامپیوتر، دانشکده فنی مهندسی، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران [e-mail]

Received: 2016-10-30 Accepted : 2016-10-30 Published : 2016-02-20

Keywords: Key words: Social simulation, Artificial society, Model of acquiring and transferring experience, Reinforcement learning,

Abstract :

Abstract Usually in multi-agent systems, interactions between agents and agents interactions with the environment would be formed as selection and implementation of operations of a limited set of specific actions by agents. Therefore, the type and complexity rate of the emergent behaviours resulting from these interactions is also dependent on the how to implementation and numbers of applicable behaviours by the agents. In the conducted research it was tried to investigate the impact of learning on improvement of agents’ behaviour in the selection of methods (strategies) of experience transfer and in improving the welfare indexes (measures) in the artificial society with the development of model of acquiring and transferring experience as well as adding learning capability to agents. Reinforcement learning was the learning method proposed in this study to increase the range of agents’ capabilities. With using this method, agents learned over time how to select and implement more appropriate actions in confrontation with different environmental conditions to be closer to the individual and social goals. The results of simulation and experiments showed that applying learning process can lead to improve behaviour of agents and improve welfare indexes (measures) in the artificial society.