Reinforcement Learning

In this type of learning, the neural networks learn based on the feedback received by it. Positive feedback helps the system to recognise right or correct output, and negative feedback helps the system to recognise incorrect.

Reinforcement learning works on a feedback-based process, in which an AI agent (A software component) automatically explore its surrounding by hitting & trail, taking action, learning from experiences, and improving its performance.

Agent gets rewarded for each good action and get punished for each bad action; hence the goal of reinforcement learning agent is to maximize the rewards.

In reinforcement learning, there is no labelled data like supervised learning, and agents learn from their experiences only.

The reinforcement learning process is similar to a human being; for example, a child learns various things by experiences in his day-to-day life. An example of reinforcement learning is to play a game, where the Game is the environment, moves of an agent at each step define states, and the goal of the agent is to get a high score. Agent receives feedback in terms of punishment and rewards.

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Applications of Reinforcement Machine Learning

• Video Games: Reinforcement Machine Learning algorithms are much popular in gaming applications. It is used to gain super-human performance. Some popular games that use RL algorithms are AlphaGO and AlphaGO Zero.
• Robotics: Reinforcement Machine Learning is widely being used in Robotics applications. Robots are used in the industrial and manufacturing area, and these robots are made more powerful with reinforcement learning.
• Text Mining: Text mining, one of the great applications of NLP, is now being implemented with the help of Reinforcement Learning