Reinforcement Learning doesnât tell you whatâs right.
It only tells you how good your choice was.
No feedback on what to do. Only on how it went.
:blobcoffee: Example: A multi-armed bandit (like a slot machine with several levers). You don't know which lever is the best - you can only find out by trying it out. Exploring means giving up a known reward (from exploitation) â in hopes of finding a better one.
This balance between exploration and exploitation is the central dilemma in reinforcement learning.
:blobcoffee: A simple strategy is Δ-greedy:
â In 90% of cases you take the best known action
â In 10% of cases, you try a different one by chance
In simulations, Δ-greedy methods perform better in the long term than pure greed (always take the supposedly best) - because they master the âexplore-exploit trade-offâ.
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