seniorRecommendation Systems
What is reinforcement learning exploration strategy?
Updated May 16, 2026
Short answer
Exploration strategies balance trying new items and exploiting known preferences in RL systems.
Deep explanation
Exploration strategies like epsilon-greedy, Thompson Sampling, and Upper Confidence Bound (UCB) are used to explore uncertain items while exploiting known good ones. This helps avoid local optima and improves long-term learning in recommendation systems.
Real-world example
Spotify occasionally playing new songs to test user interest.
Common mistakes
- Setting exploration rate too low or too high.
Follow-up questions
- What is Thompson Sampling?
- Why is exploration needed?