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Reinforcement Learning in Machine Learning Techniques.

RL is one of the most useful techniques for Machine Learning.

Reinforcement Learning is a Machine Learning technique. Basically, Reinforcement Learning is a part of three basic machine learning models with supervised learning and unsupervised learning. Here no labeled data is provided, due to which the agents have to learn robotically with the help of feedback and experiences. Thus depending on the action, the agent gets positive feedback, negative feedback, or a penalty. Normally Reinforcement Learning is used in solving problems related to chronological decision making. One of the main aims of the agent here is to get positive feedback which helps it in leveraging its performance. Hence it is a fundamental part of Artificial Intelligence and many of its concepts working is dependent on RL techniques. Due to which they are extremely helpful for AI Startups.

Terms related to Reinforcement Learning

We shall now take a look into terms that are used for reinforcement Learning.

  • Agent(): It is an operation that can distinguish the environment and take suitable actions.
  • Environment(): This refers to the circumstances in which the agent is present.
  • Action(): It is the progress done by the agent in that environment.
  • State(): Once the action is taken by the agent the situation in which it returns.
  • Reward(): It is the response given to the agent by the environment to praise its actions.
  • Policy(): It is the strategy used by the agent.
  • value(): It is the expected long-term retuned with the discount factor.
  • Q-value(): It is a value with additional parameter (a).

Ways of executing RL in Machine Learning

Basically, there are three ways of applying RL in machine learning.

  • Value-Based: Here the main aim is of getting maximum value at a state under any strategy.
  • Policy-Based: Here the main aim is to find the ideal strategy to gain maximum rewards in the future without using the value function.
  • Model-Based: For the agent to explore and learn from the environment model is created in virtual form.

Algorithms used in RL

Basically, algorithms in RL are used for Artificial Intelligence and gaming purposes.

  • Q-Learning
  • State Action Reward State Action (SARA)
  • Deep Q Neural Network (DQN)

Hence we can say that RL is one of the interesting parts of machine learning and its application in game playing, businesses, manufacturing, and finance sector can be beneficial.

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Rupika Nimbalkar

Rupika Nimbalkar

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