Exploration vs Exploitation

What Exploration vs Exploitation is

Pure exploitation locks in early, possibly wrong, conclusions; pure exploration never cashes in on what has been learned. Good systems balance the two over time.

How it works

Strategies include epsilon-greedy randomisation, upper confidence bounds and Thompson sampling, all of which allocate trials in proportion to uncertainty about each option.

Why it matters

It governs reinforcement learning, recommendation, ad allocation and experimentation budgets — anywhere a system must both learn and perform.

Common uses

• →Multi-armed bandit testing
• →Ad and content allocation
• →RL training
• →Adaptive experiments