Brief

Researchers have developed a new offline reinforcement learning algorithm called SOCD for delay-constrained scheduling in multi-user systems. This method utilizes a diffusion policy and a critic network to learn scheduling strategies solely from pre-collected data, avoiding the need for real-time system interaction. Experiments show SOCD effectively handles various system dynamics and outperforms existing scheduling approaches. AI

Researchers have introduced OMAD, a novel framework for online multi-agent reinforcement learning (MARL) that utilizes diffusion policies to enhance agent coordination. This approach addresses the challenge of intractable likelihoods in diffusion models, which typically hinder exploration in online MARL settings. OMAD employs a relaxed policy objective that maximizes scaled joint entropy and a joint distributional value function for decentralized policy optimization, leading to significant improvements in sample efficiency. AI