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没有观察噪声的损害的探索的内在奖励:基于自由能量原理的模拟研究
Theodore Jerome Tinker1, Kenji Doya2, Jun Tani3
1Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology Graduate University, Onna-san 904-0495, Okinawa, Japan theodore.tinker@oist.jp.
Neural computation
|August 6, 2024
概括
强化学习代理受益于探索奖励,如和好奇心. 基于自由能量原理的隐藏状态好奇心,提高了对好奇心陷的抵抗力.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
背景情况:
- 强化学习 (RL) 代理人通过最大化奖励来学习,需要高效的探索来发现最佳策略.
- 和各种好奇心驱动的奖励鼓励探索,但预测错误好奇心可能容易受到不可预测噪音的"好奇心陷".
- 自由能源原理 (FEP) 提供了一个理论框架,用于理解适应性系统,并可能改善RL代理行为.
研究的目的:
- 引入和评估一种新的好奇心驱动的奖励机制,隐藏状态的好奇心,以自由能量原理 (FEP) 为基础.
- 为了比较使用隐藏状态好奇心对基线,和预测错误好奇心的代理商的探索效率和稳定性.
- 研究基于FEP的方法的潜力,以提高强化学习模型的概括性和稳定性.
主要方法:
- 六种代理类型被训练在迷宫导航任务:基线,奖励,预测错误好奇心奖励,隐藏状态好奇心奖励,和它们的组合.
- 代理商被评估他们的勘探效率和易受好奇心陷的敏感性.
- 隐藏状态好奇心是由奖励代理人基于隐藏变量的预测前后概率之间的KL分歧来实现的.
主要成果:
- 和好奇心都在RL代理中显著提高了探索效率,其中和好奇心的组合特别有效.
- 使用隐藏状态好奇心的代理人与使用预测错误好奇心的代理人相比,表现出对好奇心陷的优越弹性.
- 隐藏状态好奇特工在复杂环境中导航时表现出更好的性能和稳定性.
结论:
- 隐藏状态的好奇心,源自自由能量原理,提供了一个强大的机制,用于加强学习的高效探索.
- 这种方法减轻了好奇心陷的有害影响,提高了代理商的性能和概括性.
- 在RL中实施基于FEP的原则可能会导致更强大和更适应的人工智能,潜在地反映生物学习过程.
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