27 lines
1.5 KiB
Markdown
27 lines
1.5 KiB
Markdown
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---
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title: Reinforcement Learning
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---
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#### Suggested Reading:
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<!-- Please add any articles you think might be helpful to read before writing the article -->
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- http://incompleteideas.net/sutton/book/the-book-2nd.html
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#### Reinforcement Learning
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<!-- Please add your working draft below in GitHub-flavored Markdown -->
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Reinforcement Learning refers to a field of Machine Learning that applies to agents that you reinforce by giving them reward and punishment. It gives a nice gradual learning and can simplify the learning of agent in tasks where you cannot determine a proper error value.
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Example:
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A bot is given a task to play Space Invaders, it tries to learn to play it by interacting with game and in return getting a reward for the points that it scored at end of the game. Greater the reward, greater are its chances of doing the similar gameplay. In that way, it learns how to play the game and perform in the best possible way.
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In industries robot uses deep reinforcement learning to pick a device from one box and putting it in a container. Whether it succeeds or fails, it memorizes the object and gains knowledge and train’s itself to do this job with great speed and precision. Learning on its own is a kind of reinforcement learning provided the learning is in positive dimension.
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## List of Common Algorithms
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Q-Learning
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Temporal Difference (TD)
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Deep Adversarial Networks
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## Use cases:
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Some applications of the reinforcement learning algorithms are computer played board games (Chess, Go), robotic hands, and self-driving cars.
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