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基于虫优化和优先经验重播机制的深度决定性政策梯度算法
Hengwei Zhu1, Chuiting Rong2, Haorui Liu2
1College of Computer and Information Engineering, Dezhou University, Dezhou, 253023, China. 1430059696@qq.com.
Scientific reports
|April 22, 2025
概括
本研究介绍了一种新的虫优化算法 (DBOP-DDPG),用于在连续行动空间中进行强化学习. 增强的算法提高了全球优化,加快了融合,优于现有方法.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 优化算法 优化算法
背景情况:
- 对于连续动作空间的强化学习 (RL) 算法往往遭受缓慢的融合和局部优化.
- 解决这些局限性对于在复杂的控制任务中推进RL应用至关重要.
研究的目的:
- 提出一个改进的深度决定性政策梯度 (DDPG) 算法,包括虫优化和优先级经验重复.
- 在连续行动空间中增强RL算法的全球优化能力和融合速度.
主要方法:
- 引入了甲虫优化器 (DBO) 以实现同时进行多种群搜索,防止局部优化.
- 开发了一个基于优先级的样本数据标准,用于重复体验,将数据存储在三个基于重要性的重复缓冲器中.
- 集成的DBOP-DDPG,具有优先级的经验重播机制.
主要成果:
- 拟议的DBOP-DDPG算法与基线算法相比,至少提高了10%的融合速度.
- 在OpenAI Gym环境中,累计奖励价值增加了高达150%.
- 有效地缓解了在连续行动空间RL中缓慢收和局部最佳性的问题.
结论:
- 具有优先级经验重复的DBOP-DDPG算法为在连续行动空间中的强化学习提供了显著的进步.
- 这种混合方法提高了RL代理商的效率和有效性.
- 这些发现表明机器人,自主系统和其他连续控制领域的应用范围更广泛.
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