Trust Region Policy Optimization

Trust Region Policy Optimization (TRPO) is a method in reinforcement learning used to improve an agent’s decision-making policy safely and effectively. It works by making small, controlled updates to the policy—how the agent chooses actions—so that each change doesn’t drastically reduce performance or cause instability. Think of it as setting a “trust region” or safe zone around the current policy; updates stay within this zone to ensure reliable progress. This approach balances improving the policy with maintaining stability, leading to more efficient learning in complex environments.