Understanding Exploration
Exploration is used to increase knowledge about an environment or model. The exploration process selects actions with uncertain outcomes to gather information about the possible states and rewards that the performed actions will result. The key aspects of exploration include:
- Information Gain: The main objective of exploration is to gather fresh data that can improve the model’s comprehension of the surroundings. This involves exploring distinct regions of the state space or experimenting with different actions whose outcomes are unknown.
- Uncertainty Reduction: Reducing uncertainty in the model’s estimates of the environment guides the actions that are selected. For example, activities that are rarely selected in the past are ranked in order of possible rewards.
- State Space Coverage: In certain models, especially those with large or continuous state spaces, exploration makes sure that enough different areas of the state space are visited to prevent learning that is biased toward a small number of experiences.
Exploitation and Exploration in Machine Learning
Exploration and Exploitation are methods for building effective learning algorithms that can adapt and perform optimally in different environments. This article focuses on exploitation and exploration in machine learning, and it elucidates various techniques involved.
Table of Content
- Understanding Exploitation
- Exploitation Strategies in Machine Learning
- Understanding Exploration
- Exploration Strategies in Machine Learning
- Balancing Exploitation and Exploration
- Balancing Exploration and Exploitation in Multi-Armed Bandit Problem
- Problem Setup
- Strategies Incorporating Exploration and Exploitation
- Challenges and Considerations