通过最大限度地减少自由能源来增强MCTS.
Mawaba Pascal Dao1, Adrian M Peter2
1Florida Institute of Technology, Melbourne, FL 32901, USA pdao2015@my.fit.edu.
Neural computation
|September 22, 2025
概括
这项研究引入了一种新的规划框架,将蒙特卡罗树搜索 (MCTS) 与主动推理相结合,以平衡探索和寻求奖励. 这种方法有效地减少了不确定性,同时优化了复杂环境中的目标.
科学领域:
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
- 机器人技术 机器人技术 机器人技术
背景情况:
- 基于自由能量原理的积极推理,解释了在不确定的环境中的代理行为.
- 对智能代理来说,平衡探索和以目标为导向的行动至关重要.
研究的目的:
- 开发一个新的规划框架,将蒙特卡罗树搜索 (MCTS) 与主动推理集成在一起.
- 在追求外部奖励的同时,系统地减少认识体系的不确定性.
主要方法:
- 将MCTS与主动推理目标相结合.
- 使用交叉法 (CEM) 来优化行动建议.
- 将预期的回报与获得的信息结合起来,以最大限度地减少免费能源.
主要成果:
- 拟议的规划者保持价值和不确定性的连贯估计.
- 与现有方法相比,在连续控制任务上证明了性能提升.
- 在不牺牲规划效率的情况下实现了计算可操作性.
结论:
- 新的框架成功地将探索和开发结合起来,使用主动推断和MCTS.
- 这种方法为智能代理提供了一种计算可处理的方法来应对不确定性.
- 该计划器对人工智能和机器人技术的应用具有前景,需要适应性决策.
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