Module 7Reinforcement Learning
Actor-Critic Methods
Combine policy learning with value estimation for lower-variance updates.
Why this module matters
Most practical modern RL relies on actor-critic structure in one form or another.
Prerequisites
- ▸ Policy gradients
- ▸ Value functions
Learning objectives
- ▸ Explain the actor/critic split
- ▸ Use advantage estimates
- ▸ Implement a simple A2C-style loop
Core concepts
Critic targets
Advantages
Bias-variance balance
Hands-on practice
- ▸ Train a small actor-critic baseline
Expected output
An actor-critic implementation with tracked policy/value losses.
Study checklist
- ✅ Explain the actor/critic split
- ✅ Use advantage estimates
- ✅ Implement a simple A2C-style loop
Common mistakes
- ⚠️ Using noisy critic targets carelessly
- ⚠️ Confusing value and advantage
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 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.