Exploration Strategies in Machine Learning
In the strategy called exploration, gathered data is used to extend or upgrade the model’s knowledge by considering other options’ opportunities. Some common exploration techniques in machine learning include:
- Epsilon-Greedy Exploration: Epsilon-greedy algorithms manage to unify those two characteristics (exploitation and exploration) by sometimes choosing completely random actions with probability epsilon while continuing to use the current best-known action with probability (1 – epsilon).
- Thompson Sampling: Thompson sampling exploits the Bayesian method to explore and exploit services simultaneously. It helps to keep the chances that are associated with the parameters and takes in considerations of what is most likely to happen so as to balance for exploration and exploitation.
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