Researchers Develop Implicit Neural Representations to Learn Policies from Unlabeled Behavioral Data
A new machine learning method called Behavioral INR uses implicit neural representations to identify and learn different policies from unlabeled multi-policy behavioral data without requiring manual annotations. The approach adapts techniques from computer vision to represent policies as state-action functions, naturally handling variable episode lengths and different sampling rates. This work addresses a practical problem in robotics, games, and other domains where diverse behaviors are mixed together without labels.
Researchers have introduced Behavioral INR, a self-supervised generative model that learns to identify different policies from behavioral datasets where policy labels are unavailable. The method adapts implicit neural representations (INRs)—traditionally used in vision tasks—to the behavioral domain, representing each policy as a function mapping states to actions. An episode-level latent variable modulates this function through FiLM layers, creating a generative prior that enables policy identity inference without supervision. The researchers also define policy-level out-of-distribution shifts along state-distribution and action-distribution axes, extending beyond standard behavioral OOD settings. Evaluation across synthetic data, MuJoCo simulations, and real-world datasets from chess, Formula 1 racing, robotics, and other domains shows that Behavioral INR most consistently improves policy identifiability in continuous state-action settings, particularly when episodes are longer and policy overlap is high.
What different sources said
- arXiv cs.AICenter
Implicit Neural Representations of Individual Behavior
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