Proximal Policy Optimization (PPO)

What You'll Learn

Explain why large policy updates are dangerous
Understand the trust region concept and its importance
Implement PPO with the clipped surrogate objective
Tune PPO hyperparameters effectively
Train agents using PPO on standard benchmarks

Policy gradient methods are powerful, but fragile. One bad update can destroy a good policy. TRPO solved this elegantly but with complex math. Then in 2017, OpenAI introduced PPO - a simpler algorithm that works just as well. Today, PPO is the most popular deep RL algorithm in practice.

Why PPO?

The core insight is simple: don’t let the new policy get too far from the old one.

TRPO enforces this with a KL divergence constraint solved via second-order optimization
PPO approximates this with a clipped objective that can be optimized with standard gradient descent

The result: simpler code, similar performance, and remarkable robustness.

Chapter Overview

This chapter explains PPO, the workhorse algorithm behind modern RL applications from game-playing agents to RLHF for language models.

Trust Regions

Why we need to limit how much policies can change

The PPO Algorithm

Clipped surrogate objectives explained

Why PPO Works

Simplicity, stability, and performance

PPO in Practice

Hyperparameters, tricks, and common pitfalls

The Big Picture

📖Proximal Policy Optimization

A policy gradient algorithm that constrains policy updates by clipping the probability ratio between old and new policies. This prevents destructively large updates while maintaining simplicity.

∑Mathematical Details

The PPO clipped objective:

$L^{\text{CLIP}}(\theta) = \mathbb{E}_t \left[ \min\left( r_t(\theta) \hat{A}_t, \text{clip}(r_t(\theta), 1-\epsilon, 1+\epsilon) \hat{A}_t \right) \right]$

where $r_t(\theta) = \frac{\pi_\theta(a_t|s_t)}{\pi_{\theta_{\text{old}}}(a_t|s_t)}$ is the probability ratio.

Think of PPO as a “cautious optimizer”:

If an action looks good (positive advantage), increase its probability - but not too much
If an action looks bad (negative advantage), decrease its probability - but not too much
The clip prevents overreaction to any single batch of experience

Prerequisites

This chapter assumes familiarity with:

The advantage function from Actor-Critic Methods
GAE for advantage estimation
Basic policy gradient concepts

Key Takeaways

Large policy updates can be catastrophic - trust regions prevent this
TRPO uses complex second-order optimization; PPO uses simple clipping
The clipped objective limits how much any update can change the policy
PPO is robust to hyperparameters and works across many domains
PPO powers modern applications including RLHF for language models

Next ChapterModel-Based RL→