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ChronoSRL: Temporal Geometry for Self-Supervised Reinforcement Learning
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 theref
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