自组合策略用于可扩展的持续强化学习
Self-Composing Policies for Scalable Continual Reinforcement Learning
AI总结：
本文提出一种可增长模块化神经网络架构，通过选择性组合先前策略，实现持续强化学习中避免灾难性遗忘、参数线性增长，并在基准任务上取得更优性能。
摘要：
这项工作引入了一种可增长且模块化的神经网络架构，该架构自然地避免了持续强化学习中的灾难性遗忘和干扰。每个模块的结构允许选择性地组合先前策略及其内部策略，从而加速当前任务的学习过程。与先前增长的神经网络方法不同，我们表明所提出方法的参数数量随任务数量线性增长，并且不会为了可扩展性而牺牲可塑性。在基准连续控制和视觉问题中进行的实验表明，所提出的方法比替代方法实现了更大的知识迁移和性能提升。
英文摘要：
This work introduces a growable and modular neural network architecture that naturally avoids catastrophic forgetting and interference in continual reinforcement learning. The structure of each module allows the selective combination of previous policies along with its internal policy, accelerating the learning process on the current task. Unlike previous growing neural network approaches, we show that the number of parameters of the proposed approach grows linearly with respect to the number of tasks, and does not sacrifice plasticity to scale. Experiments conducted in benchmark continuous control and visual problems reveal that the proposed approach achieves greater knowledge transfer and performance than alternative methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
