PPO stands for Proximal Policy Optimization. It is an algorithm for reinforcement learning (RL) that is designed to work well on problems with high-dimensional action spaces. PPO is a variant of the trust region policy optimization (TRPO) algorithm, which is known for its stability and performance.
The key idea behind PPO is to optimize the policy in a “proximal” region around the current policy, rather than trying to find the global optimal policy. This is done by using a “surrogate objective function” that is designed to approximate the true objective function (i.e., the expected return of the policy) in the proximal region. The surrogate objective function is designed to be easy to optimize, while still being a good approximation of the true objective.
PPO uses a technique called “clipped surrogate objective” to make sure that the new policy is not too different from the current policy. This is done by scaling the surrogate objective function by a “clipping factor” that is based on the difference between the new policy and the current policy. The clipping factor is designed to be small when the difference is small, and large when the difference is large. This prevents the new policy from deviating too far from the current policy, which helps to ensure stability.
PPO also uses a technique called “value function regularization” to improve the stability and performance of the algorithm. This is done by adding a term to the surrogate objective function that encourages the value function to be close to the true value function. This helps to reduce the variance of the estimates of the value function, which improves the stability and performance of the algorithm.
In summary, PPO is an algorithm for reinforcement learning that is designed to work well on problems with high-dimensional action spaces. It is a variant of the TRPO algorithm that uses a surrogate objective function to optimize the policy in a proximal region around the current policy, and uses techniques such as clipped surrogate objective and value function regularization to improve stability and performance.