arXiv:2604.17551v2 Announce Type: replace Abstract: 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 id
The paper presents a new, more stable approach to goal-conditioned reinforcement learning, addressing known issues with current methods that limit practical applications.
Improved stability and sample efficiency in reinforcement learning can accelerate the development and deployment of advanced AI agents capable of complex goal-oriented tasks.
This new method, SVL, offers a probabilistic, more robust framework for GCRL, potentially leading to more reliable and scalable AI systems.
- · AI researchers
- · AI developers
- · Robotics industry
- · Software companies leveraging AI
- · Companies reliant on less stable RL methods
More efficient training of AI models for complex tasks requiring goal-oriented behavior.
Accelerated development of general-purpose AI agents for various applications.
Enhanced automation capabilities across industries leading to increased productivity and shifts in labor requirements.
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Read at arXiv cs.LG