Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching
arXiv:2603.27044v3 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) is widely recognized as sample-inefficient, a limitation attributable in part to the high dimensionality and substantial functional redundancy inherent to the policy parameter space. A recent framework, which we refer to as Action-based Policy Compression (APC), mitigates this issue by compressing the parameter space $\Theta$ into a low-dimensional latent manifold $\mathcal Z$ using a learned generative mapping $g:\mathcal Z \to \Theta$. However, its performance is severely constrained by relying on imm
This research addresses a fundamental limitation in DRL, sample inefficiency, which has become more critical as AI models scale in complexity and data demands.
Improving the efficiency of DRL systems by compressing policy representation could accelerate AI development and reduce computational costs.
This research could lead to more robust and less resource-intensive methods for training AI policies, potentially expanding the applicability of reinforcement learning.
- · AI research institutions
- · Deep Reinforcement Learning applications
- · Cloud computing providers (through efficiency gains)
- · Inefficient DRL approaches
- · Hardware vendors relying solely on brute-force compute for DRL
More efficient training of complex AI agents and models.
Faster development cycles for autonomous systems in various industries.
Reduced energy consumption for large-scale AI training, positively impacting the energy bottleneck narrative.
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Read at arXiv cs.AI