Enhancing RL Performance through Regularization Techniques

Regularization techniques play a crucial role in enhancing the stability and performance of deep reinforcement learning algorithms. Recent research emphasizes the importance of managing the churn phenomenon, which can destabilize training by causing frequent changes in action selection after updates. By incorporating a KL regularizer that constrains changes during policy updates, the models can maintain better consistency and convergence across various RL algorithms. This adjustment has shown significant performance improvements, particularly in popular algorithms like PPO and DQN, demonstrating versatility in both discrete and continuous action settings.