We examine how synthetic agents interact in social environments employing a variety of agent training strategies against diverse opponents. Such agent training and playing methods indicate that quality playing relies more on the correct set-up of the learning mechanism than on experience. The experimentation provides valuable insight into the potential of an agent to compete against other agents in its environment and yet manage to also co-operate so that this particular environment allows for the emergence of a competitive champion agent, which will represent its group in further contests. Additionally, by investigating performance while constraining the number of moves we gain interesting insight into competitive learning and playing with resource constraints.
CITATION STYLE
Kiourt, C., & Kalles, D. (2016). Learning in multi agent social environments with opponent models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9571, pp. 137–144). Springer Verlag. https://doi.org/10.1007/978-3-319-33509-4_12
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