Trust Region Policy Optimization (TRPO):

Trust Region Policy Optimization (TRPO):

• https://arxiv.org/abs/1502.05477v5
• https://paperswithcode.com/method/trpo

Trust Region Policy Optimization, or TRPO, is a policy gradient method in reinforcement learning that avoids parameter updates that change the policy too much with a KL divergence constraint on the size of the policy update at each iteration.

• Advanced policy gradient method that constrains policy updates using the Kullback-Leibler (KL) divergence, ensuring that new policy improvements do not deviate too significantly from the previous policy, which helps maintain learning stability.
• Mathematically optimizes policy updates by solving a constrained optimization problem that maximizes expected returns while limiting the policy change, preventing destructive large updates that could destabilize learning.
• Provides more robust policy improvement compared to standard policy gradient methods by using a trust region constraint that guarantees monotonic policy improvement, making it particularly effective for complex reinforcement learning environments with high-dimensional action spaces.and stable compared to traditional policy gradient methods like TRPO.