  Active inference is a unifying theory for perception and action resting upon
the idea that the brain maintains an internal model of the world by minimizing
free energy. From a behavioral perspective, active inference agents can be seen
as self-evidencing beings that act to fulfill their optimistic predictions,
namely preferred outcomes or goals. In contrast, reinforcement learning
requires human-designed rewards to accomplish any desired outcome. Although
active inference could provide a more natural self-supervised objective for
control, its applicability has been limited because of the shortcomings in
scaling the approach to complex environments. In this work, we propose a
contrastive objective for active inference that strongly reduces the
computational burden in learning the agent's generative model and planning
future actions. Our method performs notably better than likelihood-based active
inference in image-based tasks, while also being computationally cheaper and
easier to train. We compare to reinforcement learning agents that have access
to human-designed reward functions, showing that our approach closely matches
their performance. Finally, we also show that contrastive methods perform
significantly better in the case of distractors in the environment and that our
method is able to generalize goals to variations in the background.

Machine Learning