Curiosity-driven reinforcement learning with homeostatic regulation
This work addresses the challenge of efficient exploration in reinforcement learning for agents in complex environments, representing an incremental improvement.
The paper tackled the problem of enhancing reinforcement learning agents' information gain in complex environments by introducing a curiosity reward based on information theory and homeostatic regulation, resulting in improved performance as validated experimentally.
We propose a curiosity reward based on information theory principles and consistent with the animal instinct to maintain certain critical parameters within a bounded range. Our experimental validation shows the added value of the additional homeostatic drive to enhance the overall information gain of a reinforcement learning agent interacting with a complex environment using continuous actions. Our method builds upon two ideas: i) To take advantage of a new Bellman-like equation of information gain and ii) to simplify the computation of the local rewards by avoiding the approximation of complex distributions over continuous states and actions.