Module 8Reinforcement Learning
Proximal Policy Optimization
Learn the clipped-objective method that dominates practical policy optimization.
Why this module matters
PPO is one of the most common bridges from RL theory to real training systems.
Prerequisites
- ▸ Actor-critic basics
Learning objectives
- ▸ Understand clipping and trust-region intuition
- ▸ Use GAE
- ▸ Tune rollout and minibatch settings
Core concepts
Clipped surrogate objective
Entropy bonus
GAE
Hands-on practice
- ▸ Build and train a PPO loop on continuous control
Expected output
A PPO trainer with rollout diagnostics.
Study checklist
- ✅ Understand clipping and trust-region intuition
- ✅ Use GAE
- ✅ Tune rollout and minibatch settings
Common mistakes
- ⚠️ Too many epochs per batch
- ⚠️ No observation normalization
- ⚠️ Treating entropy bonus as decoration
Module rhythm
- 1. Read the summary and why-it-matters section first.
- 2. Work through concepts before rushing into practice.
- 3. Use the checklist to verify real understanding, not just completion.
How to continue
Next you will turn scratch algorithms into reproducible engineering workflows.
Back to course overview →How to use this page well
Treat each module as a compact learning system: understand the intuition, verify the concepts, do one hands-on task, then use the checklist and mistakes section to pressure-test your understanding.