Flow-Based Reinforcement Learning

This paper presents a novel Flow-based reinforcement learning strategy to model agent systems that can adapt to complex and dynamic problem environments by incrementally mastering their skills. It is inspired by the psychological notion of Flow that describes the optimal mental state experienced by an individual when they are fully immersed in a task and find it intrinsically rewarding to engage with. The proposed model presents an algorithm to describe the Flow experience such that agents can be trained through finer distinctions to the challenges across training time to maintain them in the Flow zone. In contrast to the traditional and incremental learning approaches that suffer from limitations associated with overfitting, the Flow-based model drives agent behaviours not simply through external goals but also through intrinsic curiosity to improve their skills and thus the performance levels. Experimental evaluations are conducted across two simulation environments on a maze navigation task and a reward collection task with comparisons against a generic reinforcement learning model and an incremental reinforcement learning model. The results reveal that these two models are prone to overfit under different design decisions and loose the ability to perform in dynamic variations of the tasks in varying degrees. Conversely, the proposed Flow-based model is capable of achieving near optimal solutions with random environmental factors, appropriately utilising the previously learned knowledge to identify robust solutions to complex problems.