DQN and DDQN are trained to choose offloading amounts in a simulated Metaverse digital twin, and near-maximum reward is presented as evidence of suitability.
A reinforcement learning-based adaptive path tracking approach for autonomous driving,
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Maximizing the Promptness of Metaverse Systems using Edge Computing by Deep Reinforcement Learning
DQN and DDQN are trained to choose offloading amounts in a simulated Metaverse digital twin, and near-maximum reward is presented as evidence of suitability.