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 thinsg in order to expand its knowledge (exploration). The former is a low risk option, but may miss longer-term gains -- an exmaple of a local minimum}. The agent therefore needs {meta-heuristics}} in order to manage this trade-off.
Used on Chap. 15: page 362; Chap. 16: pages 378, 379, 388; Chap. 22: page 547
Also known as exploitation, exploration