ChronoSRL: Temporal Geometry for Self-Supervised Reinforcement Learning
Quick summary
arXiv:2609.36238v1 Announce Type: new Abstract: A goal that is close in space can be far away in time. Obstacles, terrain, and the agent's own capabilities determine how long it takes to get there. Yet, critics in contrastive and survival reinforcement learning do not measure the distances in their representation space in units of time. We therefore introduce ChronoSRL, which gives the critic's embeddings an explicit temporal geometry. The distance between state-action and goal embeddings is trained to match the time that the agent takes to reach the goal (goal-reaching time), while goals that
Key takeaways
- arXiv:2609.36238v1 Announce Type: new Abstract: A goal that is close in space can be far away in time.
- Obstacles, terrain, and the agent's own capabilities determine how long it takes to get there.
- Yet, critics in contrastive and survival reinforcement learning do not measure the distances in their representation space in units of time.
Why it matters
The importance of “ChronoSRL: Temporal Geometry for Self-Supervised Reinforcement Learning” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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