DQN Improvements
What You'll Learn
- Identify limitations of vanilla DQN
- Explain Double DQN and why it fixes overestimation bias
- Describe Prioritized Experience Replay and its benefits
- Understand Dueling Networks architecture and its intuition
- Explain how Rainbow combines improvements for state-of-the-art performance
DQN was a breakthrough, but it wasn’t perfect. The Q-values it learned were systematically too high, it wasted time relearning easy transitions, and it couldn’t distinguish between good states and good actions.
Each of these problems sparked an improvement, and combining them all created Rainbow, one of the most sample-efficient value-based agents.
DQN’s Limitations
After the initial DQN success, researchers identified several ways to improve it:
- Overestimation bias: The max operator causes Q-values to be too high
- Uniform sampling: Not all transitions are equally useful for learning
- Entangled values: State value and action advantages are learned together
Chapter Overview
Double DQN
Fixing overestimation bias
Prioritized Replay
Learning more from important transitions
Dueling Networks
Separating state value from action advantage
Rainbow
The sum is greater than its parts
The Key Insight
Each DQN improvement addresses a specific, identifiable problem. The elegance lies in their complementary nature: they can be combined for compounding benefits.
Each improvement we’ll cover solves a specific problem:
- Double DQN: Uses two networks to decouple action selection from evaluation
- Prioritized Experience Replay: Samples important transitions more frequently
- Dueling Networks: Separates learning “how good is this state?” from “which action is best?”
- Rainbow: Combines six improvements into one powerful agent
Prerequisites
This chapter builds directly on:
- Deep Q-Networks for the base algorithm we’re improving
Key Questions We’ll Answer
- Why do DQN’s Q-values tend to be too high?
- How can we prioritize learning from surprising experiences?
- When does it matter to separate state value from action value?
- Do all these improvements stack together?
Key Takeaways
- Double DQN fixes overestimation with a simple change to the target computation
- Prioritized Experience Replay focuses learning on high-error transitions
- Dueling Networks explicitly separate state value from action advantages
- Rainbow combines six improvements, showing they complement each other
- Each improvement is independently valuable; together they’re even better