Trust Region Policy Optimization (TRPO) is a type of algorithm used in reinforcement learning to improve the training of policies. The main goal of TRPO is to update the policy in a way that maximizes expected rewards while ensuring that the changes made to the policy during training do not deviate significantly from the current policy. This is done to prevent the policy from deteriorating after updates, a common issue when using other optimization methods. TRPO employs what's called a trust region, which essentially constrains the update within a "trustworthy" space, allowing for stable and reliable learning.
The method works by formulating the policy update as a constrained optimization problem. Specifically, TRPO maximizes the expected reward while enforcing a constraint that limits how much the new policy can diverge from the old one. Mathematically, this is done using a technique called the Kullback-Leibler (KL) divergence, which quantifies the difference between the old and new policies. By ensuring that the KL divergence does not exceed a specified threshold, TRPO keeps the updates within a safe range. This prevents actions that could have adverse effects, such as drastic changes that could lead to worse performance.
A practical example of TRPO can be seen in environments like simulated robotics or gaming. In these cases, TRPO can be employed to fine-tune a policy that controls robot movements or game character actions. The algorithm allows for significant exploration of new strategies while maintaining a core of previously learned behaviors. This balance makes TRPO particularly useful for complex environments where small changes can lead to significant performance differences. Overall, TRPO provides a structured and reliable way to optimize policies in reinforcement learning while minimizing the risks of poor updates.