Can you see how I learn? Human observers' inferences about Reinforcement Learning agents' learning processes
Researchers studied how humans interpret the behavior of reinforcement learning agents. They found that people's understanding of these agents' learning processes can be broken down into four core themes: agent goals, knowledge, decision-making, and learning mechanisms. The study used a novel paradigm to assess human inferences about agent learning and involved two experiments with 43 participants. The findings suggest ways to design more interpretable reinforcement learning
Researchers studied how humans interpret the behavior of reinforcement learning agents. They found that people's understanding of these agents' learning processes can be broken down into four core themes: agent goals, knowledge, decision-making, and learning mechanisms. The study used a novel paradigm to assess human inferences about agent learning and involved two experiments with 43 participants. The findings suggest ways to design more interpretable reinforcement learning systems and improve transparency in human-robot interaction.
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Why it matters: Understanding how humans interpret reinforcement learning agents' behavior is crucial for developing transparent and trustworthy AI systems, particularly in applications like human-robot collaboration.
Source: https://arxiv.org/abs/2506.13583
This article was originally published at: https://arxiv.org/abs/2506.13583