The exploration-exploitation dilemma, also known as the explore-exploit tradeoff, is a fundamental concept in decision-making that arises in many domains. It is depicted as the balancing act between two opposing strategies. Exploitation involves choosing the best-known option based on past experiences, while exploration involves trying out new options that may lead to better outcomes in the future. Finding the optimal balance between these two strategies is a crucial challenge in many decision-making situations, where the goal is to maximize long-term benefits.

Application in machine learning

In the context of machine learning, the exploration-exploitation tradeoff is often encountered in

reinforcement learning Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine ...

, a type of machine learning that involves training agents to make decisions based on feedback from the environment.Richard S. Sutton; Andrew G. Barto (2020). Reinforcement Learning: An Introduction (2nd edition). http://incompleteideas.net/book/the-book-2nd.html The agent must decide whether to exploit the current best-known policy or explore new policies to improve its performance. Various algorithms have been developed to address this challenge, such as epsilon-greedy, Thompson sampling, and the

upper confidence bound Upper may refer to: * Shoe upper or ''vamp'', the part of a shoe on the top of the foot * Stimulant, drugs which induce temporary improvements in either mental or physical function or both * ''Upper'', the original film title for the 2013 found fo ...

References

Machine learning Strategy Cognition {{Comp-sci-stub