arXiv:2605.25740v1 Announce Type: new Abstract: Offline goal-conditioned reinforcement learning (GCRL) provides a practical framework for obtaining goal-reaching policies from fixed datasets. However, learning a reliable goal-conditioned value function in long-horizon tasks remains challenging. In this paper, we identify erroneous generalization in goal-conditioned value functions as a fundamental bottleneck, and demonstrate that appropriate inductive bias in the value function is crucial for addressing the bottleneck. Building on these findings, we propose Latent-Aligned Value Learning (LAVL)
The continuous drive for more autonomous and capable AI systems in complex, real-world scenarios makes advancements in reinforcement learning, especially for goal-conditioned tasks, highly relevant.
Improving offline goal-conditioned reinforcement learning (GCRL) removes a significant bottleneck in developing AI agents that can reliably learn from existing data to achieve long-term objectives across diverse domains.
The proposed LAVL framework offers a method to overcome erroneous generalization in value functions, which could lead to more robust and deployable AI agents capable of handling long-horizon, complex tasks.
- · AI research labs
- · Robotics companies
- · Generative AI platforms
- · Logistics and automation sector
- · Companies relying on manual, repetitive tasks
- · AI systems with poor generalization in GCRL
Improved performance of AI agents in complex, goal-oriented tasks using existing datasets.
Accelerated development of more sophisticated autonomous systems capable of learning from diverse, pre-recorded experiences without extensive online experimentation.
Enhanced AI agent capabilities could lead to new applications in simulation, control, and automated decision-making across industries, reducing the need for costly real-world trial-and-error.
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Read at arXiv cs.LG