Hierarchical hybrid modeling for flexible tool use
Matteo Priorelli Ivilin Peev Stoianov
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.