Reinforced Imitation Learning by Free Energy Principle
This work addresses the challenge of efficient learning in sparse-reward environments for AI agents, though it appears incremental as it builds on existing RL and IL methods with a novel theoretical framework.
The paper tackles the problem of high exploration costs in reinforcement learning and the limitations of imitation learning by unifying them using the Free Energy Principle, resulting in an algorithm that reduces exploration costs and achieves higher performance than suboptimal experts in sparse-reward visual control tasks.
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.