Trust Region Policy Optimization (TRPO) is a reinforcement learning algorithm designed to improve the training of policies in a stable and efficient manner. The primary goal of TRPO is to optimize a policy by ensuring that the updates made are not too large, which can destabilize the training process. It achieves this by constraining the step size of policy updates to remain within a "trust region," thus preventing too drastic changes in behavior with each gradient descent iteration.
The core of TRPO's approach is the use of the concept of a trust region, which is defined by a distance metric known as the Kullback-Leibler (KL) divergence. This metric quantifies how much the new policy differs from the old one. TRPO enforces a constraint during the optimization process, ensuring that the KL divergence between the new and old policy remains below a predetermined threshold. This constraint helps maintain a balance between exploring new strategies and relying on the previously learned information, which is crucial for stability during training.
In practical terms, TRPO works by conducting multiple epochs of sampling from the environment to gather trajectories and calculate the policy update. It uses natural gradient descent to optimize the policy while respecting the KL constraint. This method is particularly beneficial in high-dimensional action spaces, as it allows for more reliable convergence compared to regular policy gradient methods. An example application of TRPO can be found in robotic control tasks, where ensuring stable performance is critical, and overly aggressive updates could lead to erratic behavior.