arXiv:2605.26012v1 Announce Type: new Abstract: Deep reinforcement learning (RL) agents commonly rely on high-dimensional neural representations, despite growing evidence that task-relevant value and policy structure may be intrinsically low-dimensional. In this work, we present a simple yet effective representation-level prior that inserts a fixed orthonormal projection to constrain encoder features to a low-dimensional subspace, requiring no auxiliary objectives, pretraining, or changes to the underlying RL algorithm. Under a linear realizability assumption, we prove that when the bottleneck
This research addresses a fundamental challenge in deep reinforcement learning (RL) regarding the inherent complexity of neural representations and the desire for more efficient, low-dimensional solutions.
Improving the efficiency and interpretability of RL agents through low-dimensional representations can accelerate AI development and reduce computational overheads for complex AI systems.
The proposed method offers a simpler, more robust way to constrain RL encoder features without complex additions, potentially streamlining the training and deployment of advanced AI agents.
- · AI researchers
- · Reinforcement learning developers
- · Hardware providers for AI
- · Companies relying on brute-force high-dimensional RL
More efficient and resource-friendly reinforcement learning models become feasible.
This could lead to a broader application of sophisticated AI agents in environments with limited computational resources.
Reduced compute demands for advanced AI could lessen the energy footprint of AI development and deployment.
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Read at arXiv cs.LG