A Progressive Design Study of Visual Encoders and Value Estimation for Replay-Free Parallelized Q-Learning
Replay-free parallelized Q-learning removes the large experience replay buffers and target networks used by conventional deep Q-learning, but the role of network architecture in this training regime remains comparatively underexplored. We investigate this question through a progressive three-phase study within the Parallelized Q-Network (PQN) framework. First, we compare eight convolutional encoder topologies on Atari-57 under a common training protocol while jointly considering performance and computational complexity. Second, we integrate Hadamax-style multiplicative feature interactions and explicit pooling into the selected encoder hierarchy. Third, with the visual representation fixe...
| Authors | Taha Shieenavaz, Shabnam Zareshahraki, Loris Nanni |
|---|---|
| Primary category | cs.LG |
| Published | 7 August 2026 |
| Last updated at source | 25 September 2026 |
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