New framework learns state representations from trajectories without rewards

Researchers have developed a novel state representation framework for Markov decision processes (MDPs) that learns directly from state trajectories without needing reward signals or explicit action data. This method focuses on learning the minimum action distance (MAD) between states, which quantifies the fewest actions required to move between them. By creating an embedding space where distances reflect MAD, this approach facilitates downstream tasks like goal-conditioned reinforcement learning and reward shaping, demonstrating superior performance over existing state representation techniques across various environments. AI

IMPACT This research could enable more efficient learning in reinforcement learning agents by providing a structured understanding of state transitions without explicit reward supervision.

RANK_REASON Academic paper detailing a new method for state representation in reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Hugging Face
• Lorenzo Steccanella
• Markov decision processes
• minimum action distance

• paper
• other

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