Introduction to Reinforcement learning

Reinforcement Learning is a major area of interest within the field of machine learning. Its ability to learn without input data is opening new opportunities in different areas such as robotics and gaming.

What is Reinforcement Learning?

Reinforcement Learning is a type of Machine Learning technique based on Learning by feedback.

Reinforcement Learning is a type of machine learning approaches in which an intelligent agent interacts with its surroundings and learns how to behave within this environment.

How does Reinforcement Learning work

The agent learns to behave by interacting with the environment and evaluating the results.

The agent gets good feedback for good action and bad feedback for bad action.

Based on this feedback mechanism, the agent will learn by itself without the need for labeled data and this is the main difference between Reinforcement Learning and Supervised Learning.

Decision-making in this type is sequential which means output depends on the state of the current input, and the next input depends on the output of the previous input.

Elements of Reinforcement Learning

• Agent: a system that interacts with the environment and tries to complete a task within this environment.
• Action: is the mechanism that allows the agent to interact in its environment.
• State: the representation of the current environment of the task, in other words, the observations that the agent receives from the environment.
• Policy: a strategy that dictates the agent’s actions or the decision-making process.
• The reward function is an incentive mechanism that tells the agent what is correct and incorrect by using reward and punishment.
• Value function represents how good is a state for an agent to be in and the probability of receiving a future reward.

Practical applications of Reinforcement Learning:

• Manufacturing
• Inventory Management.
• Power Systems
• Finance

Why using reinforcement Learning

• Advantages of Reinforcement Learning:
• It does not require large labeled datasets.
• It can come up with new solutions never considered by humans.
• It brings results while improving, online Learning.
• goal-oriented, by using sequence-based feedback rather than an input-output method.