ppo

PPO most commonly refers to Proximal Policy Optimization, a family of on-policy policy gradient methods in reinforcement learning designed to train agents to maximize cumulative reward. Introduced by Schulman and colleagues in 2017, PPO aims to improve stability and sample efficiency over earlier policy gradient approaches while remaining simpler to implement than more restrictive methods such as TRPO.

The core idea of PPO is to update the policy by optimizing a surrogate objective that constrains the extent of policy change at each update. This is typically achieved with a clipping mechanism: the algorithm maximizes the minimum of the unclipped objective and a clipped objective that limits the probability ratio between the new and old policies. In mathematical terms, the ratio r_t(θ) = π_θ(a_t|s_t) / π_{θ_old}(a_t|s_t) is used with the advantage A_t to form a surrogate that discourages large policy updates. PPO often combines this with advantages estimated by methods like Generalized Advantage Estimation (GAE) and performs multiple epochs of minibatch updates on a fixed batch of on-policy data.

Two common variants exist: the clipped objective PPO-clip and a KL-penalized version that introduces a penalty for deviating from the previous policy. PPO is valued for its balance of simplicity, robustness to hyperparameters, and strong performance across discrete and continuous action spaces. It has become a standard baseline in robotics, gaming, and simulated control tasks, frequently implemented in major RL libraries.

In other contexts, PPO can also stand for terms such as Preferred Provider Organization in health insurance, but in reinforcement learning it denotes Proximal Policy Optimization.