We present the design of an autoregressive active inference agent in the form of message passing on a factor graph. Expected free energy is derived and distributed across a planning graph. The proposed agent is validated on a robot navigation task, demonstrating exploration and exploitation in a continuous-valued observation space with bounded continuous-valued actions. Compared to a classical optimal controller, the agent modulates action based on predictive uncertainty, arriving later but with a better model of the robot's dynamics.

翻译：我们提出了一种基于因子图上消息传递的自回归主动推理智能体设计。期望自由能在规划图上被推导并分布式计算。该智能体在机器人导航任务上得到验证，展示了其在连续值观测空间中以有界连续值动作进行探索与利用的能力。与经典最优控制器相比，该智能体能够基于预测不确定性调节动作，虽抵达时间稍晚，但获得了更精确的机器人动力学模型。