Enhancing Stability and Convergence in Deep Reinforcement Learning

This chapter explores innovative research aimed at improving the robustness and convergence of deep reinforcement learning algorithms. It discusses a method to stabilize trained DRL policies and presents a novel regularization technique to address state-action churn during training.