有效的贝叶斯政策重用与可扩展的观察模型在深度强化学习学习
概括
本研究介绍了一种改进的贝叶斯政策再利用 (BPR) 方法,用于在深度强化学习 (DRL) 中有效地转移政策. 通过使用状态过渡样本和可扩展的观察模型,它可以更快,更准确地推断任务,并避免在持续学习中负面转移.
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:
- 贝叶斯政策重用 (BPR) 是一种使用任务信念推断的政策转移框架.
- 现有的BPR方法往往依赖于有限的,剧集结束信号,如剧集性回归.
- 当前的BPR方法可能是样本效率低下的,并且与可扩展的观察模型作斗争.
研究的目的:
- 加强贝叶斯政策再利用 (BPR) 以提高深度强化学习 (DRL) 中政策转移效率.
- 解决观察信号的局限性和传统 BPR 中观察模型的可扩展性.
- 适应BPR的持续学习场景,防止负面转移.
主要方法:
- 用信息性的,即时的状态转换样本取代了插曲性回归,用于任务推断.
- 开发了一个可扩展的观察模型,通过从有限的样本中调整状态过渡函数.
- 将框架扩展到持续学习,使用插入和运行可扩展的观察模型.
主要成果:
- 与传统的BPR相比,拟议的方法实现了更快,更准确的任务推断.
- 可扩展的观察模型有效地将任务概括为最小样本.
- 持续学习的延伸成功地减轻了在新的,未知的任务中负面转移.
结论:
- 改进的BPR方法显著提高了DRL的政策转移效率.
- 新的观测信号和可扩展模型为现实世界的应用提供了实际优势.
- 适应持续学习扩大了BPR对动态环境的适用性.
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