exploration-exploitation trade-off

Terms from Artificial Intelligence: humans at the heart of algorithms

When interacting with the world in reinforcement learning, an agent has to choose whether to take the best action based on its existing knowledge (exploitation) or to try new things in order to expand its knowledge (exploration). The former is a low risk option, but may miss longer-term gains – an example of a local minimum}. The agent therefore needs meta-heuristics in order to manage this trade-off.

Used in Chap. 15: page 232; Chap. 16: pages 242, 248; Chap. 22: page 352

Also known as exploitation, exploration