This is Part 4 of our 5-part Reinforcement Learning series. We're covering the most widely-used RL algorithm in production today.
Series Overview:
- Part 1: RL Basics — MDP, Bellman Equation, Value Functions
- Part 2: From Q-Learning to DQN
- Part 3: Policy Gradient Methods
- Part 4: PPO — The Industry Standard (You are here)
- Part 5: SAC — Mastering Continuous Control
Why PPO Matters
Proximal Policy Optimization (Schulman et al., 2017) is the default algorithm for:
- RLHF in ChatGPT, Claude, and other LLMs
- Game AI — OpenAI Five (Dota 2), hide-and-seek agents
- Robotics — manipulation, locomotion
- Production RL — anywhere stability matters more than sample efficiency
Why? Because PPO is stable, simple to implement, and works across a wide range of problems with minimal hyperparameter tuning.
The Problem PPO Solves
In Part 3, we saw that policy gradient methods compute:
∇J(θ) = E [ ∇log π_θ(a|s) · A(s,a) ]
The issue: step size matters enormously. Too small = slow learning. Too large = the policy changes drastically, performance collapses, and it never recovers.
TRPO (Trust Region Policy Optimization) solved this with a hard constraint on policy change — but it required second-order optimization (computing the Fisher information matrix), making it complex and expensive.
PPO achieves similar stability with a simple clipping trick that requires zero additional computation beyond standard gradient descent.
Continue reading the full article on TildAlice
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