Trust Region Policy Optimization (TRPO) is a policy optimization method for reinforcement learning that aims to improve the stability of policy updates in policy gradient methods.
In policy gradient methods, such as the popular REINFORCE algorithm, the policy is updated by computing the gradient of the expected return with respect to policy parameters and updating the parameters in the direction of the gradient. However, while this approach can work well in theory, in practice it can lead to unstable and ineffective learning, resulting in slow convergence or even divergence.
TRPO addresses this issue by constraining the size of policy updates to a trust region, which is a region around the current policy where the performance is expected to improve. By limiting the size of policy updates, TRPO is able to prevent large and unstable updates that can result in the policy diverging.
In more technical terms, TRPO constrains the maximum KL divergence between the new and old policies, which ensures that the policy updates are incremental and conservative. Moreover, TRPO computes an approximate solution to the constrained optimization problem by performing conjugate gradient iterations, which scales well to high-dimensional parameter spaces.
To understand this better, let’s consider an example. Imagine a robot learning to walk. In the early stages of learning, the policy is likely to be stochastic and unpredictable, leading to unstable updates. However, by constraining the policy changes within a trust region, we can ensure that the updates are small and incremental, allowing the robot to explore and learn in a more stable manner.
In summary, TRPO improves the stability of policy updates in policy gradient methods by constraining policy changes within a trust region, which prevents large and unstable updates that can lead to slow convergence or even instability.