https://www.forbes.com/sites/bernardmarr/2018/10/22/artificial-intelligence-whats-the-difference-between-deep-learning-and-reinforcement-learning/#901273271e1b

University of Michigan - Ann Arbor

Deep learning analyses a training set, identifies complex patterns and applies them to new data. 

Reinforcement learning works sequentially in an unknown environment━taking an action, evaluating the rewards, and adjusting the following actions accordingly.

Deep learning and reinforcement learning complement each other:

 - Reinforcement learning algorithms manage the sequential process of taking an action, evaluating the result, and selecting the next best action. However, they need a good mechanism to select the best action based on previous interactions.
 - Deep learning can be that mechanism━it is the most powerful method available today to learn the best outcome based on previous data.

 

Deep Learning vs. Reinforcement Learning

Deep Reinforcement Learning (DRL) is a technology that combines the two, creating a sequential reinforcement learning process, in which deep learning determines the action taken at every stage.

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