Definition
The multi-armed bandit problem illustrates the dilemma faced by an agent deciding whether to stick with a known rewarding option (exploitation) or try new options to discover potentially better rewards (exploration). Named after hypothetical slot machines with multiple arms, each offering different payout probabilities, this framework is fundamental to online decision-making processes. Algorithms like epsilon-greedy, UCB, and Thompson Sampling are used to solve this problem efficiently, optimizing long-term cumulative reward in dynamic environments.
Summary
A multi-armed bandit is a classical problem in probability theory and reinforcement learning that models the trade-off between exploration and exploitation.
Key Concepts
- Exploration vs Exploitation
- Reward Maximization
- Stochastic Processes
- Regret Minimization
Use Cases
- A/B testing optimization
- Recommendation systems
- Ad placement strategies