Decoding behavior with minimal and interpretable agent models

Engineering notes

Key topics: autonomous driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Understanding how living organisms process sensory information from their surroundings and translate it into decisions is a fundamental problem across biological scales - from biochemical signalling in single-cells to neural computations in animal brains. In this work, we address this challenge by introducing a method to reconstruct general decision processes directly from behavioral observations alone. Our approach is applicable to any biological agent and does not require prior knowledge of its internal mechanisms or its environment. Our agent model is defined by a recurrent dynamics over a discrete set of internal states which encode and process sensory information, and dictate which actions to execute. We validate our method on synthetic agents and demonstrate that we can exactly recover the agent's behavior for non-trivial tasks. Then, we infer agent models from experimental data of rats performing evidence accumulation and of mice making decisions under uncertainty and in changing environments. In both cases, very few internal states suffice to reproduce the observed behavior with high accuracy. Crucially, the immediate interpretability of the inferred dynamics allows to understand the computational process underlying decision-making.

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