Enhancing Trust in Reinforcement Learning Algorithms

Trust in reinforcement learning algorithms like Proximal Policy Optimization (PPO) can be compromised by the deterioration of representations due to non-stationarity. This research demonstrates that when representations collapse, the trust region becomes ineffective, failing to differentiate between states and degrading performance. By implementing a method that regularizes these representations, the effectiveness of the trust region can be preserved, leading to improved algorithm reliability. This highlights the importance of maintaining robust representations within the framework to ensure consistent performance.