连续扩散器 (CoD):掌握连续离线RL与经验排练练习
IEEE transactions on neural networks and learning systems
|September 16, 2025
概括
持续扩散器 (CoD) 通过使用基于排练的扩散模型来解决强化学习 (RL) 中的可塑性-稳定性权衡问题. 这种方法使代理人能够适应新的任务,同时有效地保留先前获得的知识.
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:
- 静态数据集在游戏和QA的人工神经网络中很常见.
- 强化学习 (RL) 等现实世界的应用包括动态的,顺序的任务.
- 在保持知识的同时适应不断变化的任务,提出了可塑性-稳定性权衡挑战.
研究的目的:
- 提出一种基于排练的新型连续扩散模型,连续扩散器 (CoD).
- 增强在连续任务中快速适应 (可塑性) 和知识保留 (稳定性) 的代理能力.
- 在多样化的基准上对现有方法进行COD的绩效评估.
主要方法:
- 开发了一个线下基准,包含多个领域的90个任务.
- 使用顺序建模和条件生成训练了连续扩散器 (CoD) 模型.
- 通过保存和重放以前数据集的子集来实现一个排练缓冲区.
主要成果:
- CoD显示出一个有利的可塑性-稳定性权衡.
- 该模型在大多数任务中表现优于现有的基于扩散的方法和其他基线.
- 实验证实了CoD在顺序任务学习中的有效性.
结论:
- 拟议的持续扩散器 (CoD) 在持续学习场景中有效平衡了可塑性和稳定性.
- 对于在动态环境中运行的RL代理来说,CoD提供了一个有前途的解决方案.
- 与当前的扩散模型和基线相比,该方法显示出更高的性能.
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