<p>The goal of <strong>machine learning</strong> methods is to learn rules from data and make predictions and/or decisions based on them. </p>

<p>The learning process can be done in a(n) <strong>supervised</strong>, <strong>semi-supervised</strong>, <strong>unsupervised</strong>, <strong>reinforcement</strong> learning fashion.  </p>

<p>In <strong>reinforcement learning</strong> (RL), an agent interacts with an environment and learns an optimal policy, by trial and error (using reward points for successful actions and penalties for errors). It is used in sequential decision making problems [1].</p>

<p><strong>Deep learning</strong> as a sub-field of machine learning is a mathematical framework for learning latent rules in the data or new representations of the data at hand. The term "deep" refer to the number of learning layers in the framework. Deep learning can be used with any of aforementioned learning strategies, i.e.,  supervised, semi-supervised, unsupervised, and reinforcement learning. </p>

<p>A <strong>deep reinforcement learning</strong> technique is obtained when deep learning is utilized by any of the components of reinforcement learning [1].  Note that <strong>Q-learning</strong> is a component of RL used to tell an agent that what action needs to be taken in what situation. Detailed information can be found in [1].</p>

<p>[1] Li, Yuxi. "Deep reinforcement learning: An overview." arXiv preprint arXiv:1701.07274 (2017).</p>
What's the difference between reinforcement learning, deep learning, and deep reinforcement learning?
<p>What's the difference between reinforcement learning, deep learning, and deep reinforcement learning? Where does Q-learning fit in?</p>