生成性的对抗性软行为者-批判性
概括
本研究介绍了用于深度强化学习的生成对抗软行为者-批评 (GASAC). GASAC能够学习多式联运政策,并提高持续控制任务的稳定性.
科学领域:
- 深度强化学习学习 (deep reinforcement learning) 是一种深度强化学习的方法.
- 连续控制 控制 连续控制
- 政策优化 政策优化
背景情况:
- 标准软演员-批评 (SAC) 使用因子化的高斯政策,限制表达力和忽视行动相关性.
- 学习随机和多模式策略对于复杂的深度强化学习任务至关重要.
研究的目的:
- 增强SAC中的政策代表性,超越单模高斯分布.
- 开发一种更稳定,更具表现力的政策学习方法,用于持续控制.
主要方法:
- 使用变量定理的变化,重新审视SAC政策的规范化流.
- 提出一个依赖于状态的非体积保存 (SD-NVP) 架构.
- 引入生成对抗软演员-批评 (GASAC) 具有生成对抗损失.
主要成果:
- GASAC有效地模拟多式联运政策,捕捉复杂的行动分布.
- 提出的方法在累积回报方面显示了更稳定的学习.
- 在多目标和MuJoCo连续控制任务上进行实验验证.
结论:
- GASAC为学习表达式和多式联络政策提供了一个强大的标准SAC替代方案.
- 该方法在具有挑战性的连续控制环境中提高了学习稳定性和性能.
- 规范化流动和生成对抗方法对于推进深度强化学习政策是有效的.
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