Gatsby Computational Neuroscience Unit, University College London, 17 Queen Square, London WC1N 3AR, United Kingdom.
Curr Opin Neurobiol. 2012 Dec;22(6):1068-74. doi: 10.1016/j.conb.2012.05.011. Epub 2012 Jun 15.
Reinforcement learning (RL) has become a dominant computational paradigm for modeling psychological and neural aspects of affectively charged decision-making tasks. RL is normally construed in terms of the interaction between a subject and its environment, with the former emitting actions, and the latter providing stimuli, and appetitive and aversive reinforcement. However, there is recent emphasis on redrawing the boundary between the two, with the organism constructing its own notion of reward, punishment and state, and with internal actions, such as the gating of working memory, being treated on an equal footing with external manipulation of the environment. We review recent work in this area, focusing on cognitive control.
强化学习 (RL) 已成为一种占主导地位的计算范式，可用于对情感决策任务的心理和神经方面进行建模。RL 通常被理解为主体与其环境之间的相互作用，前者发出动作，后者提供刺激、奖励和惩罚。然而，最近人们越来越重视重新划定两者之间的界限，即生物体构建自己的奖励、惩罚和状态概念，以及内部动作（例如工作记忆的门控）与外部环境的操作被平等对待。我们回顾了该领域的最新工作，重点是认知控制。
