Beyond Winning and Losing: Modeling Human Motivations and Behaviors Using Inverse Reinforcement Learning

1 Jul 2018Baoxiang WangTongfang SunXianjun Sam Zheng

In recent years, reinforcement learning (RL) methods have been applied to model gameplay with great success, achieving super-human performance in various environments, such as Atari, Go, and Poker. However, those studies mostly focus on winning the game and have largely ignored the rich and complex human motivations, which are essential for understanding different players' diverse behaviors... (read more)

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