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420 training runs of a multi-agent reinforcement learning algorithm for power router energy management are documented. The dataset contains experimental results from an ablation study comparing an improved Proximal Policy Optimization algorithm with a double-buffer mechanism against other models. It was authored by Junyan Lyu and uploaded to figshare in April 2026.
Data is provided in a single XLS (Excel) file. The 5.5 KB size confirms it is a summary of results, not a large-scale training dataset.