迈向高性能、内存高效且快速的强化学习——来自决策神经科学的经验教训。
Toward high-performance, memory-efficient, and fast reinforcement learning-Lessons from decision neuroscience.
作者信息
Lee Jee Hang, Seymour Ben, Leibo Joel Z, An Su Jin, Lee Sang Wan
机构信息
Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.
KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea.
出版信息
Sci Robot. 2019 Jan 16;4(26). doi: 10.1126/scirobotics.aav2975.
PMID:33137757
Abstract
Recent insights from decision neuroscience raise hope for the development of intelligent brain-inspired solutions to robot learning in real dynamic environments full of noise and unpredictability.
摘要
决策神经科学的最新见解为开发受大脑启发的智能解决方案带来了希望，这些解决方案可用于在充满噪声和不可预测性的真实动态环境中进行机器人学习。
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