ChronoSRL learns separate embeddings for state-action pairs and goals.
Temporal distance improves self-supervised goal-directed reinforcement learning
ChronoSRL represents goals by estimated travel time, then trains policies to reach them quickly, reliably and persistently.
Research Lab
Nico Bohlinger · Jan Peters
Technical University of Darmstadt · Robotics Institute Germany (RIG) · German Research Center for AI (DFKI) · hessian.AI
Research Digest··3 min read
Bohlinger and Peters introduce ChronoSRL, a self-supervised reinforcement learning method whose learned representation measures distance in units of goal-reaching time rather than physical proximity or an unconstrained similarity score.
Why this paper
From German Research Center for AI (DFKI) and 3 others · Part of Credit Assignment in Agentic RL, now 30 papers
In one line
ChronoSRL gives critic embeddings temporal geometry, training distances to match goal-reaching times for faster and more reliable learning.
What we could check
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