SVL: Goal-Conditioned Reinforcement Learning as Survival Learning
Standard approaches to goal-conditioned reinforcement learning (GCRL) that rely on temporal-difference learning can be unstable and sample-inefficient due to bootstrapping. While recent work has explored contrastive and supervised formulations to improve stability, we present a probabilistic alternative, called survival value learning (SVL), that reframes GCRL as a survival learning problem by modeling the time-to-goal from each state as a probability distribution. This structured distributional Monte Carlo perspective yields a closed-form identity that expresses the goal-conditioned value function as a discounted sum of survival probabilities, enabling value estimation via a hazard model...
| Authors | Franki Nguimatsia Tiofack, Fabian Schramm, Théotime Le Hellard et al. |
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| Primary category | cs.LG |
| Published | 19 April 2026 |
| Last updated at source | 29 May 2026 |
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