Active Inference is a theory of action arising from neuroscience which casts action and planning as a bayesian inference problem to be solved by minimizing a single quantity - the variational free energy. Active Inference promises a unifying account of action and perception coupled with a biologically plausible process theory. Despite these potential advantages, current implementations of Active Inference can only handle small, discrete policy and state-spaces and typically require the environmental dynamics to be known. In this paper we propose a novel deep Active Inference algorithm which approximates key densities using deep neural networks as flexible function approximators, which enables Active Inference to scale to significantly larger and more complex tasks. We demonstrate our approach on a suite of OpenAIGym benchmark tasks and obtain performance comparable with common reinforcement learning baselines. Moreover, our algorithm shows similarities with maximum entropy reinforcement learning and the policy gradients algorithm, which reveals interesting connections between the Active Inference framework and reinforcement learning.

该文章介绍了Active Inference的理论，探讨了将行动和规划转化为一个贝叶斯推理问题以最小化可变自由能的方法。 它提出了一种新颖的深度Active Inference算法，该算法通过使用深度神经网络作为灵活的函数逼近器来逼近关键密度，从而使Active Inference能够处理更大更复杂的任务，并展示了与强化学习的有趣关联。

深度主动推理与变分策略梯度