{
  "id": 17923,
  "url": "https://arxiv.org/abs/2608.07335v1",
  "title": "Aftab: A Comprehensive Benchmark of CNN Encoders and Advanced Value Functions in Parallelized Q-Networks",
  "summary": "Recent advancements in deep reinforcement learning have increasingly favored simplified, highly parallelized paradigms. Notably, the Parallelized Q-Network (PQN) algorithm achieves stable off-policy learning without relying on computationally expensive replay buffers or target networks. However, the representational capacity and parameter efficiency of visual encoders operating in these buffer-free settings remain underexplored. In this work, we systematically investigate the architectural desig",
  "authors": "Taha Shieenavaz, Shabnam Zareshahraki, Loris Nanni",
  "category": "research",
  "topics": "regulation,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-07T15:29:15.000Z",
  "fetched_at": "2026-08-10T05:10:00.488Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/17923",
  "original_url": "https://arxiv.org/abs/2608.07335v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}