University of Kent, School of Computing, Canterbury CT2 7NZ, U.K.
University of Birmingham, School of Psychology, Birmingham B15 2TT, U.K.
Neural Comput. 2022 Sep 12;34(10):2132-2144. doi: 10.1162/neco_a_01529.
Branching time active inference is a framework proposing to look at planning as a form of Bayesian model expansion. Its root can be found in active inference, a neuroscientific framework widely used for brain modeling, as well as in Monte Carlo tree search, a method broadly applied in the reinforcement learning literature. Up to now, the inference of the latent variables was carried out by taking advantage of the flexibility offered by variational message passing, an iterative process that can be understood as sending messages along the edges of a factor graph. In this letter, we harness the efficiency of an alternative method for inference, Bayesian filtering, which does not require the iteration of the update equations until convergence of the variational free energy. Instead, this scheme alternates between two phases: integration of evidence and prediction of future states. Both phases can be performed efficiently, and this provides a forty times speedup over the state of the art.
分支时间主动推理是一个将规划视为贝叶斯模型扩展形式的框架。它的根源可以追溯到主动推理，这是一个广泛用于大脑建模的神经科学框架，以及在强化学习文献中广泛应用的蒙特卡罗树搜索方法。到目前为止，通过利用变分信息传递提供的灵活性来推断潜在变量，这是一个迭代过程，可以理解为沿着因子图的边缘发送消息。在这封信中，我们利用了一种替代的推断方法，即贝叶斯滤波的效率，它不需要迭代更新方程，直到变分自由能收敛。相反，该方案在两个阶段之间交替进行：证据的整合和未来状态的预测。这两个阶段都可以有效地进行，这比现有技术快四十倍。
