Exploration is the trial-and-error side of reinforcement learning: the system tries random actions, watches the feedback, and uses that data to learn what seems worth doing. section 1.27
A simple ad-targeting setup makes it concrete. A platform can show random ads first, record which ones get clicks, then narrow in on the kinds of ads a user seems to prefer. section 1.27
The idea sits inside a larger tradeoff.
- Exploration gathers new information. section 1.27
- Exploitation uses what has already been learned to show relevant actions. section 1.27
- The hard part is balancing the two over time, since even a learned policy may still need occasional random actions. section 1.27
It is a practical but wasteful learning strategy, and the material notes that commercial use of plain reinforcement learning is rare for that reason. section 1.27
