数百指导数百万:适应性离线强化学习与专家指导的辅助学习
IEEE transactions on neural networks and learning systems
|November 7, 2023
概括
本研究介绍了引导的线下强化学习 (RL),以解决数据分布问题. 通过适应地调整每个数据样本的政策约束,它显著提高了RL算法性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 线下强化学习 (RL) 训练代理人使用预先收集的数据集,而无需实时交互.
- 线下RL的一个关键挑战是分布式转移问题,训练和部署数据的分布不同.
- 当前的方法通常使用统一的政策约束,这可能不是所有数据点的最佳.
研究的目的:
- 为线下强化学习提出一种新的方法,解决统一政策约束的局限性.
- 引入一种方法,根据个别数据样本特征,以适应性调整政策约束.
- 为了提高离线RL算法的性能和稳定性.
主要方法:
- 引入了导向离线增强学习 (GORL),一种插件式方法.
- 开发了一个使用专家演示的指导网络.
- 指导网络适应性地确定了每个样本的政策改进和政策约束之间的平衡.
主要成果:
- 从理论上证明了GORL指导机制的合理性和接近最佳性.
- 通过广泛的实验,在各种环境中展示了显著的性能改进.
- 展示了GORL在与现有的线下RL算法集成时的兼容性和有效性.
结论:
- 适应性政策约束对于减轻线下RL的分配转移至关重要.
- GORL提供了一种灵活有效的解决方案,用于改善线下RL性能.
- 拟议的方法提供了统计学上显著的好处,并且很容易适用于各种线下RL框架.
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