What is Exploration vs. Exploitation in Reinforcement Learning?

“Exploration and Exploitation dilemma” is one of the key concepts in reinforcement learning. There are a lot of deep explanations elsewhere so here I’d like to share tips on what you can say during an interview setting.

What is exploration vs. exploitation in reinforcement learning?

Here are some example answers for readers’ reference:

As in reinforcement learning, the agent is not aware of the different states, actions for each state, associate rewards, and transition to the next state, but it learns it by exploring the environment. However, the knowledge of an agent about the state, actions, rewards, and resulting states is partial, and this results in Exploration-Exploitation Dilemma.

Exploitation is defined as a greedy approach in which agents try to get more rewards by using estimated value but not the actual value. So, in this technique, agents make the best decision based on current information. Unlike exploitation, in exploration techniques, agents primarily focus on improving their knowledge about each action instead of getting more rewards so that they can get long-term benefits. So…