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NePPO: Near-Potential Policy Optimization for General-Sum Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) is increasingly used to design learning-enabled agents that interact in shared environments. However, training MARL algorithms in general-sum games remains challenging: learning dynamics can become unstable, and convergence guarantees typically hold only in
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