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Convex-Concave Reinforcement Learning 论文发布

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2026年10月8日，arXiv 机器学习理论板块发布论文《Convex-Concave Reinforcement Learning》，提出 Convex-Concave RL（CCRL）方法。该方法在 log-density-ratio 坐标下，将逐迭代目标表示为 difference-of-convex-constrained difference-of-convex 规划，并用 sequential convex programming 求解，同时给出收敛保证。目前公开信息仅涉及该论文的方法与理论结果，未见实验或后续验证报道。

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LatestOct 8
arXiv 发布 CCRL 论文，将策略优化重构为 DC 约束 DC 规划并给出收敛保证。

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Oct 8, 2026
  1. arXiv · Machine Learning Theory
    Convex-Concave Reinforcement Learning：把策略优化重构为 DC 约束 DC 规划

    论文提出 Convex-Concave RL（CCRL），在 log-density-ratio 坐标下把逐迭代目标表示为 difference-of-convex-constrained difference-of-convex 规划，并用 sequential convex programming 求解、给出收敛保证。

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