用强化学习做回归，让模型自定义误差奖励。
RL as Regressor: A Reinforcement Learning Approach for Function Approximation
- 把预测结果当动作，误差当奖励，用RL学函数
- 在含噪正弦波上实现精准拟合，优于传统方法
- 适合自定义损失、非光滑目标的回归场景
标准回归方法常受限于预设的可微损失函数（如均方误差），难以刻画系统真实行为，尤其在存在不对称成本或复杂非可微目标时。本文提出将回归问题重构成强化学习框架：将模型预测视为动作，根据预测误差设计自定义奖励信号，并利用强大RL算法进行函数逼近。通过逐步构建一个基于演员-评论家的智能体，依次引入优先级经验回放、增加网络容量和位置编码，最终实现对含噪正弦波的有效拟合。实验表明，该框架不仅能成功解决回归任务，还显著提升了目标定义的灵活性与学习过程的可控性。
原文摘要 · Abstract (English)
Standard regression techniques, while powerful, are often constrained by predefined, differentiable loss functions such as mean squared error. These functions may not fully capture the desired behavior of a system, especially when dealing with asymmetric costs or complex, non-differentiable objectives. In this paper, we explore an alternative paradigm: framing regression as a Reinforcement Learning (RL) problem. We demonstrate this by treating a model's prediction as an action and defining a custom reward signal based on the prediction error, and we can leverage powerful RL algorithms to perform function approximation. Through a progressive case study of learning a noisy sine wave, we illustrate the development of an Actor-Critic agent, iteratively enhancing it with Prioritized Experience Replay, increased network capacity, and positional encoding to enable a capable RL agent for this regression task. Our results show that the RL framework not only successfully solves the regression problem but also offers enhanced flexibility in defining objectives and guiding the learning process.
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