综合行为通过直接优化恒温的出现
Naoto Yoshida1, Tatsuya Daikoku2, Yukie Nagai3
1Graduate School of Information Science and Technology, The University of Tokyo, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan; International Research Center for Neurointelligence (WPI-IRCN), Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
概括
本研究介绍了自主代理的扩展的恒常强化学习 (RL),通过整合内部状态和环境需求来实现生存行为. 确定了最佳奖励定义,代理商表现出适应性行为和视觉突出性.
科学领域:
- 计算神经科学是一种计算神经科学.
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能是人工智能.
背景情况:
- 恒温对于生物来说至关重要,以保持稳定的内部生理状态.
- 顺势强化学习 (RL) 通过将内部动态与环境因素相结合来解释动物的行为.
- 由于复杂的合动力学,现有的恒常性RL模型的规模有限.
研究的目的:
- 开发一种可扩展的恒常性RL方法,使用深度RL.
- 为了确定最佳的奖励函数适用于恒常学.
- 创建基准环境,用于评估同居性RL剂.
主要方法:
- 采用深度强化学习实现了扩大规模的恒常性RL.
- 评估了各种奖励定义,确定驱动功能的差异是最佳的.
- 开发了两种用于培训和分析代理人的基准环境.
- 扩展了该方法,将视觉输入纳入使用深 convolutional 神经网络.
主要成果:
- 经过训练的代理人表现出受其内部生理状态影响的适应性行为.
- 驱动功能差异奖励定义产生了卓越的性能.
- 代理人表现出与生存相关的视觉突出性和多模式内部表示.
结论:
- 拟议的深度RL方法使自主行为体能够扩大自主静态学习的规模.
- 这些发现为开发具有综合生存行为的机器人提供了基础.
- 该研究强调了内部状态驱动行为和视觉处理对代理物生存的重要性.
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