Hierarchical hybrid modeling for flexible tool use

In a recent computational framework called active inference, discrete models can be linked to their continuous counterparts to perform decision-making in changing environments. From another perspective, simple agents can be combined to better capture the causal relationships of the world. How can we use these two features together to achieve efficient goal-directed behavior? We present an architecture composed of several hybrid -- continuous and discrete -- units replicating the agent's configuration, controlled by a high-level discrete model that achieves dynamic planning and synchronized behavior. Additional factorizations within each level allow to represent hierarchically other agents and objects in relation to the self. We evaluate this hierarchical hybrid model on a non-trivial task: reaching a moving object after having picked a moving tool. This study extends past work on control as inference and proposes an alternative direction to deep reinforcement learning.