This work combines the free energy principle and the ensuing active inference dynamics with recent advances in variational inference in deep generative models, and evolution strategies to introduce the "deep active inference" agent. This agent minimises a variational free energy bound on the average surprise of its sensations, which is motivated by a homeostatic argument. It does so by optimising the parameters of a generative latent variable model of its sensory inputs, together with a variational density approximating the posterior distribution over the latent variables, given its observations, and by acting on its environment to actively sample input that is likely under this generative model. The internal dynamics of the agent are implemented using deep and recurrent neural networks, as used in machine learning, making the deep active inference agent a scalable and very flexible class of active inference agent. Using the mountain car problem, we show how goal-directed behaviour can be implemented by defining appropriate priors on the latent states in the agent's model. Furthermore, we show that the deep active inference agent can learn a generative model of the environment, which can be sampled from to understand the agent's beliefs about the environment and its interaction therewith.

中文翻译：

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深度主动推理。

这项工作结合了自由能原理和随之而来的主动推理动力学，以及深度生成模型中变分推理的最新进展，以及引入了“深度主动推理”代理的演化策略。这种药物可以最大限度地减少因其体内平均感觉而产生的变化自由能，而这种自由度是由体内平衡引起的。它通过优化其感官输入的生成潜变量模型的参数，并根据其观测值，优化近似于潜变量后验分布的变化密度，并通过作用于其环境来主动采样可能在以下情况下发生的输入这种生成模型。代理的内部动态是通过深度和递归神经网络实现的，如机器学习中所使用的，使深度主动推理代理成为可扩展且非常灵活的主动推理代理类。使用山车问题，我们展示了如何通过在代理模型的潜在状态上定义适当的先验条件来实现目标导向的行为。此外，我们表明，深度主动推理主体可以学习环境的生成模型，可以从中抽取模型以了解主体关于环境及其与环境的相互作用的信念。