arXiv:2606.12200v1 Announce Type: new Abstract: We study policy representation learning from unlabeled multi-policy behavioral data. Each episode is generated by a fixed policy, but policy labels are unavailable. This setting appears in robotics play, demonstrations, games, racing, and other datasets where heterogeneous behaviors are mixed without annotations. We introduce \emph{Behavioral INR}, a self-supervised generative model that adapts implicit neural representations (INRs) from vision to behavior. Instead of mapping coordinates to RGB values, Behavioral INR represents a policy as a stat
The paper leverages recent advancements in Implicit Neural Representations (INRs) from computer vision, applying them to the challenging problem of unsupervised behavioral policy learning at a time of rapid AI development.
This research addresses a fundamental challenge in AI, enabling the learning of diverse, unlabeled behaviors, which is crucial for developing more adaptable and autonomous systems across various applications.
The ability to represent and learn individual policies without explicit labels allows for more robust and generalizable AI systems that can operate in complex, ambiguous environments.
- · Robotics industry
- · Autonomous systems developers
- · AI researchers in reinforcement learning
- · Game development
- · Tasks requiring extensive manual policy annotation
- · AI models reliant on highly structured, labeled behavioral datasets
More efficient and scalable training of complex AI behaviors in environments like robotics.
Accelerated development of AI agents capable of learning from raw, mixed behavioral data without human intervention.
Enhanced versatility and autonomy of AI systems, potentially leading to more sophisticated and nuanced human-AI interaction or fully autonomous agents in unstructured environments.
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Read at arXiv cs.LG