Trust Region Policy Optimization
Trust Region Policy Optimization (TRPO) is a reinforcement learning algorithm designed to optimize policies in a stable and efficient manner by constraining policy updates within a trust region. It uses a surrogate objective function and enforces a constraint on the Kullback-Leibler (KL) divergence between the old and new policies to prevent large, destabilizing updates. This approach helps avoid performance collapses common in policy gradient methods, making it suitable for complex, high-dimensional environments.
Developers should learn TRPO when working on reinforcement learning projects that require stable policy optimization, such as robotics, game AI, or autonomous systems, where large policy updates can lead to catastrophic failures. It is particularly useful in continuous action spaces and when using neural network policies, as it provides theoretical guarantees for monotonic improvement. TRPO is a foundational algorithm that informs later methods like Proximal Policy Optimization (PPO), making it essential for understanding modern RL advancements.