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一个适应性环境生成器有效的决策区域扩大在深度强化学习强化学习
IEEE transactions on neural networks and learning systems
|January 28, 2026
概括
本研究介绍了用于深度强化学习 (DRL) 的适应性环境生成器 (AEG). AEG使用高斯混合模型 (GMM) 动态调整培训环境的难度,改进代理通用化和决策区域.
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:
- 深度强化学习 (DRL) 代理对现实应用需要强大的概括能力.
- 域随机化是一种常见的技术,但在可参数化的环境中采样均可能导致后期阶段的训练效率低下.
- 非最佳环境采样限制了代理人的有效决策区域的扩大.
研究的目的:
- 开发一种新的方法,以动态生成适应代理人的学习进度的培训环境.
- 克服DRL域随机化中统一随机抽样的局限性.
- 提高DRL工作人员的概括能力和决策能力.
主要方法:
- 根据代理人在培训期间的长期回报,定义了环境困难.
- 提出了一个适应性环境生成器 (AEG),利用高斯混合模型 (GMM).
- 根据环境困难和代理进度,AEG通过从GMM或均分布中取样来动态生成环境.
主要成果:
- 基于AEG的培训加速了早期学习,并在后期阶段引入了更具挑战性的环境.
- 拟议的方法证明了在多种环境中具有普遍适用性.
- 与基线方法相比,通过AEG培训的代理人获得了更广泛的有效决策区域覆盖范围.
结论:
- 适应性环境生成器 (AEG) 有效地将培训环境定制为对象学习,增强泛化.
- AEG解决了DRL域随机化中统一采样效率低下的问题.
- 这种方法可以提高DRL工作人员的性能和更广泛的决策能力.
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