  Reinforcement Learning (RL) requires a large amount of exploration especially
in sparse-reward settings. Imitation Learning (IL) can learn from expert
demonstrations without exploration, but it never exceeds the expert's
performance and is also vulnerable to distributional shift between
demonstration and execution. In this paper, we radically unify RL and IL based
on Free Energy Principle (FEP). FEP is a unified Bayesian theory of the brain
that explains perception, action and model learning by a common fundamental
principle. We present a theoretical extension of FEP and derive an algorithm in
which an agent learns the world model that internalizes expert demonstrations
and at the same time uses the model to infer the current and future states and
actions that maximize rewards. The algorithm thus reduces exploration costs by
partially imitating experts as well as maximizing its return in a seamless way,
resulting in a higher performance than the suboptimal expert. Our experimental
results show that this approach is promising in visual control tasks especially
in sparse-reward environments.
