Direction-Conditioned Policies via Compositional Subgoal Scoring for Online Goal-Conditioned Reinforcement Learning
arXiv:2606.16515v1 Announce Type: cross Abstract: Hamilton-Jacobi-Bellman theory implies that the optimal goal-conditioned action depends on the goal only through the gradient of the goal-reaching distance at the current state, yet standard online GCRL still conditions the actor on the raw goal -- a signal that is geometrically uninformative when the goal is far from the data distribution. We propose Direction-Conditioned Policies (DCP), a fully online method that decomposes goal-reaching into two components sharing one InfoNCE representation $\psi$: a subgoal-scoring step that selects a visit
Ongoing advancements in reinforcement learning research continue to push the boundaries of AI capabilities, seeking more efficient and robust goal-conditioned policies.
Improved online goal-conditioned reinforcement learning methods like DCP could significantly accelerate the development of more autonomous and adaptive AI agents and robotic systems.
Conditional policies become more geometrically informed and efficient by decomposing goal-reaching into subgoal scoring, potentially leading to faster training and deployment of complex AI behaviors.
- · AI agents developers
- · Robotics industry
- · Generative AI platforms
- · AI systems requiring extensive manual programming
- · Inefficient goal-conditioned RL methods
This research directly advances the technical frontier of goal-conditioned reinforcement learning for complex tasks.
More robust and efficient AI agents could be deployed in real-world scenarios, automating tasks previously considered too complex.
The widespread adoption of highly adaptive AI agents could transform various industries by increasing automation and reducing the need for human intervention in routine intelligent tasks.
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Read at arXiv cs.AI