Data-Density guided Reinforcement Learning

This paper investigates reinforcement learning by avoiding low-density state regions using modified reward functions.The approach leverages data-density models within the state space, enabling a custom reward function that penalizes transitions into sparse regions.Applied in the Pendulum environment, this method encourages exploration in wellsampled areas while avoiding less-explored states.Empirical results show that this method effectively balances reward optimization with state confidence, enabling robust policy learning in challenging environments.