TRPO is designed to improve the stability and performance of reinforcement learning algorithms by maintaining a trust region during policy updates. This is achieved by restricting the step size of policy updates to stay within a predefined region, ensuring that each new policy does not deviate significantly from the previous one. This helps to avoid large drops in performance, a common problem in reinforcement learning when policies are updated too aggressively. The algorithm uses a constraint on the Kullback-Leibler (KL) divergence between the old and new policies, which ensures that updates are small and controlled, leading to more stable learning. TRPO is particularly useful in environments with high-dimensional action spaces and continuous control problems, where maintaining stability is crucial for effective learning.
Historical Overview: TRPO was introduced in 2015 by John Schulman and his colleagues at OpenAI. It quickly gained popularity in the reinforcement learning community due to its ability to achieve stable performance improvements and its effectiveness in complex environments.
Key Contributors: The development of TRPO is primarily credited to John Schulman, who was a researcher at OpenAI at the time. His work, along with contributions from other researchers at OpenAI, has significantly influenced the field of reinforcement learning, providing a foundation for further advancements in policy optimization methods.