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سی و دومین کنفرانس ملی و دهمین کنفرانس بین المللی مهندسی زیست پزشکی ایران
Short-term gains vs. long-term Success: Reward strategy design for reinforcement learning in football
Authors :
Mohammad Pashaei
1
Amirhossein Tayebi
2
Hadi Amiri
3
Ali Fahim
4
1- Department of Engineering Science, University of Tehran, Tehran, Iran
2- Department of Engineering Science, University of Tehran, Tehran, Iran
3- Department of Engineering Science, University of Tehran, Tehran, Iran
4- Department of Engineering Science, University of Tehran, Tehran, Iran
Keywords :
Reinforcement Learning،Multi-agent systems،Soccer Simulation
Abstract :
Reinforcement learning in complex games like soccer relies heavily on how you define your reward function and environment. In this work, we developed a custom 3v3 soccer environment and implemented two RL-based teams with distinct learning trends: one with a fast convergence but limited long-term adaptation, and another with a slower yet more robust learning trajectory. Simulation shows that despite performing better at the start, the short-term agents fall short of the performance of the long-term agents in the long run, and after passing 50% of the episodes, the win rate of long-term agents rises from 30% in the beginning to 50%.
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