拓展主动推断领域：感知-动作循环中的更多内在动机
Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop
AI总结：
本文探讨主动推断中是否可利用其他内在动机替代原有动机，同时保持核心机制，并通过形式化方法连接通用强化学习。
摘要：
主动推断是一种雄心勃勃的理论，将自主代理的感知、推断和动作选择统一于单一原则下。它为许多认知现象提供了生物合理解释，包括意识。在主动推断中，动作选择由一个评估未来动作的客观函数驱动，该函数基于当前推断的世界信念。主动推断本质上独立于外在奖励，使其在不同环境或代理形态中具有高度鲁棒性。在文献中，共享这种独立性的范式被总结为内在动机。与主动推断不同，这些动机模型通常不承诺特定的推断和动作选择机制。本文研究主动推断的推断和动作选择机制是否也可用于其他内在动机替代原动机。感知-动作循环明确将推断和动作选择与环境和代理记忆联系起来，因此被用作分析基础。我们重构了主动推断方法，将其原始公式定位其中，并展示如何在保持许多原始特征的同时使用其他内在动机。此外，我们通过形式化方法展示了与通用强化学习的联系。主动推断研究可能从比较其他内在动机诱导的动力学中受益。内在动机研究可能从另一种实现内在动机代理的方式中受益，该方式也共享主动推断的生物合理性。
英文摘要：
Active inference is an ambitious theory that treats perception, inference and action selection of autonomous agents under the heading of a single principle. It suggests biologically plausible explanations for many cognitive phenomena, including consciousness. In active inference, action selection is driven by an objective function that evaluates possible future actions with respect to current, inferred beliefs about the world. Active inference at its core is independent from extrinsic rewards, resulting in a high level of robustness across e.g.\ different environments or agent morphologies. In the literature, paradigms that share this independence have been summarised under the notion of intrinsic motivations. In general and in contrast to active inference, these models of motivation come without a commitment to particular inference and action selection mechanisms. In this article, we study if the inference and action selection machinery of active inference can also be used by alternatives to the originally included intrinsic motivation. The perception-action loop explicitly relates inference and action selection to the environment and agent memory, and is consequently used as foundation for our analysis. We reconstruct the active inference approach, locate the original formulation within, and show how alternative intrinsic motivations can be used while keeping many of the original features intact. Furthermore, we illustrate the connection to universal reinforcement learning by means of our formalism. Active inference research may profit from comparisons of the dynamics induced by alternative intrinsic motivations. Research on intrinsic motivations may profit from an additional way to implement intrinsically motivated agents that also share the biological plausibility of active inference.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
