D2RL wraps a DDPG agent with diffusion-based generators for states, actions, and rewards, and reports training-time savings in a full-duplex wireless simulation.
Optimal power allocation for rate splitting communications with deep reinforcement learning,
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Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models
D2RL wraps a DDPG agent with diffusion-based generators for states, actions, and rewards, and reports training-time savings in a full-duplex wireless simulation.