Trust Region Policy Optimization (TRPO)

Trust Region Policy Optimization (TRPO) is a reinforcement learning method that improves decision-making policies while ensuring stability. It works by making small, carefully measured updates to the policy—how an AI agent chooses actions—so it doesn't change too drastically at once. This approach uses a mathematical "trust region" to limit updates within a safe range, balancing learning progress and safety. As a result, TRPO enables efficient and reliable policy improvements, leading to better performance without causing unpredictable behavior during training.