LazyAct:基于受约束的MDP的动态状态跳转的惰演员.
Hongjie Zhang1, Zhenyu Chen1, Hourui Deng1
1College of Computer Science, Sichuan Normal University, Chengdu, China.
PloS one
|February 6, 2025
概括
LazyAct通过智能跳过非关键状态来降低深度强化学习的计算成本. 这种算法大大减少了推断,为代理商节省了时间和计算资源.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度强化学习学习 (deep reinforcement learning) 是一种深度强化学习的方法.
背景情况:
- 深度强化学习 (DRL) 在复杂的决策方面表现出色,但其计算成本很高.
- 当前的DRL方法需要对每个决策进行完整的神经网络计算,从而增加与相互作用和代理相关的成本.
研究的目的:
- 介绍LazyAct算法以减少DRL中的推理计算.
- 保持政策性能,同时显著降低计算需求.
主要方法:
- 开发了LazyAct,灵感来自于人类的推理,可以绕过非关键状态.
- 整合了一个国家跳过分支机构进入演员网络.
- 使用IMPALA和MAPPO框架为单代理和多代理推理建立了优化目标.
- 为政策网络培训提供雇员预培训和微调.
主要成果:
- LazyAct在单剂环境中减少了约80%的推断,在多剂环境中减少了约40%.
- 与现有方法相比,政策绩效保持稳定.
- 观察到任务完成时间和浮点操作 (FLOP) 显著减少.
结论:
- 通过减少计算开销,LazyAct为DRL提供了一种高效的方法.
- 该算法通过降低资源需求而不会牺牲性能来证明其实际适用性.
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