好奇探索者:政策学习中的一个可验证的探索策略
IEEE transactions on pattern analysis and machine intelligence
|September 16, 2024
概括
好奇探索器通过改善国家空间覆盖来增强政策梯度方法,这对于在具有挑战性的探索任务中实现最佳性能至关重要. 这一策略提高了像REINFORCE这样的算法的收率和样本效率.
科学领域:
- 强化学习是一种强化学习.
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 政策梯度方法严重依赖于国家覆盖假设以获得最佳绩效.
- 在网络学习或固定状态重启等环境中,不可行的覆盖假设阻碍了经典算法 (例如,REINFORCE).
- 这导致在具有挑战性的勘探场景中收率和样本效率较差.
研究的目的:
- 介绍好奇探索者,一个代的纯探索策略,旨在改善国家空间覆盖范围.
- 解决政策梯度方法的局限性,当违反覆盖假设时.
- 为了提高强化学习代理人在艰难探索任务中的表现.
主要方法:
- 好奇探索者利用重新启动分配 (r) 和内在奖励来产生越来越多的探索政策.
- 它反复改进政策,以改善基于国家访问分布的覆盖范围.
- 在没有覆盖假设的情况下,为最佳政策状态访问和REINFORCE的返回错误得出理论边界.
主要成果:
- 建立了对未经探索的国家最佳政策访问的理论上限.
- 在没有覆盖假设的情况下确定了REINFORCE的回报误差的边界.
- 废弃性研究表明,好奇探索器能够提高REINFORCE和TRPO在艰难探索任务中的性能.
结论:
- 好奇探索器有效地改善了政策梯度方法的州空间覆盖率.
- 该战略提高了融合和采样效率,特别是在具有挑战性的勘探要求的环境中.
- 它提供了一种可行的解决方案,用于在复杂场景中改进各种政策梯度算法.
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