exploration-exploitation trade-off

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.