AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Results show that visual topology, multiplicative representation, and downstream value-estimation design can substantially affect replay-free Q-learning, and that their benefits should be evaluated jointly with computational complexity.
Recent advancements in deep reinforcement learning have increasingly favored simplified, highly parallelized paradigms. Notably, the Parallelized Q-Network (PQN) algorithm enables off-policy value learning without relying on experience replay buffers or target networks. However, the representational capacity and comput...
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