Bibliographic Details
| Title: |
On the Interplay Between Sparsity and Training in Deep Reinforcement Learning |
| Authors: |
Davelouis, Fatima, Martin, John D., Bowling, Michael |
| Publication Year: |
2025 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Machine Learning, Computer Science - Artificial Intelligence |
| More Details: |
We study the benefits of different sparse architectures for deep reinforcement learning. In particular, we focus on image-based domains where spatially-biased and fully-connected architectures are common. Using these and several other architectures of equal capacity, we show that sparse structure has a significant effect on learning performance. We also observe that choosing the best sparse architecture for a given domain depends on whether the hidden layer weights are fixed or learned. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2501.16729 |
| Accession Number: |
edsarx.2501.16729 |
| Database: |
arXiv |
