Aftab: A Comprehensive Benchmark of CNN Encoders and Advanced Value Functions in Parallelized Q-Networks
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
Record details
Published: 7 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Regulation · Finance, VC & PE
Retrieved: 10 August 2026
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ethics.ai (7 August 2026), “Aftab: A Comprehensive Benchmark of CNN Encoders and Advanced Value Functions in Parallelized Q-Networks,” evidence record 17923, https://ethics.ai/record/17923 (originally published by arXiv cs.AI).
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