Reinforcement learning frustrates humans in teamplay, MIT study finds

Artificial intelligence has proven that complicated board and video games are no longer the exclusive domain of the human mind. From chess to Go to StarCraft, AI systems that use reinforcement learning algorithms have outperformed human world champions in recent years.

But despite the high individual performance of RL agents, they can become frustrating teammates when paired with human players, according to a study by AI researchers at MIT Lincoln Laboratory. The study, which involved cooperation between humans and AI agents in the card game Hanabi, shows that players prefer the classic and predictable rule-based AI systems over complex RL systems.

The findings, presented in a paper published on arXiv, highlight some of the underexplored challenges of applying reinforcement learning to real-world situations and can have important implications for the future development of AI systems that are meant to cooperate with humans.

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