Exploration–exploitation dilemma
term_id: explorationexploitation_dilemma
Category: training_techniques
Definition
In decision-making processes, agents face a trade-off: they can exploit current knowledge to get the best immediate reward, or explore unknown options to potentially find better long-term strategies. Too much exploitation leads to suboptimal solutions, while too much exploration wastes resources. Strategies like epsilon-greedy, Upper Confidence Bound (UCB), and Thompson Sampling are used to balance this trade-off effectively, ensuring the agent converges to optimal behavior without missing out on high-reward opportunities.
Summary
The exploration-exploitation dilemma is a fundamental problem in reinforcement learning where an agent must choose between exploring new actions to gather information and exploiting known actions to maximize reward.
Key Concepts
- Reward Maximization
- Epsilon-Greedy
- Upper Confidence Bound
- Reinforcement Learning
Use Cases
- Recommendation systems deciding whether to suggest popular items or new ones
- A/B testing in marketing campaigns
- Resource allocation in dynamic environments