https://arshadhs.github.io/tags/drl/Recent content in DRL on Arshad SiddiquiHugoen-ushttps://arshadhs.github.io/docs/ai/045-deep-reinforcement-learning/010-intro-to-reinforcement-learning/Mon, 01 Jan 0001 00:00:00 +0000https://arshadhs.github.io/docs/ai/045-deep-reinforcement-learning/010-intro-to-reinforcement-learning/<h1 id="reinforcement-learning"> Reinforcement Learning <a class="anchor" href="#reinforcement-learning">#</a> </h1> <p>Reinforcement Learning (RL) is a learning approach in which an <strong>agent</strong> improves its behaviour by interacting with an <strong>environment</strong> and observing the rewards produced by its actions.</p> <p>Unlike supervised learning, the agent is not given a correct action label for every situation. It must discover useful behaviour through trial, feedback, and repeated interaction.</p> <span style="color: blue;"> <strong>Reinforcement learning is goal-oriented learning through interaction.</strong> </span> <hr> <h2 id="why-reinforcement-learning"> Why Reinforcement Learning? <a class="anchor" href="#why-reinforcement-learning">#</a> </h2> <p>RL is useful when decisions affect what happens next and the quality of an action may only become clear later.</p>