Exploration vs Exploitation
Reinforcement Learning (RL) is the branch of machine learning where an 'agent' (the actor, in an environment) learns by trying an action, observing the result, and adjusting its behaviour accordingly to achieve its goal. It is not unlike teaching a dog a new trick: the dog hears a command, tries something, and a treat either arrives or it does not, and over enough repetitions the dog works out which action gets the reward. This creates a hard engineering problem, because the agent has to decide how much to stick with actions that are already working versus how much to keep testing other options. This is a concept known as the 'exploration-exploitation trade-off'. Understandably, when you don't know what you don't know, it's tricky knowing how well you're doing and if you should change tactics…
