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With the advent of technology, human has started to function by largely depending on machines and technology. To make things far easier, automated technology has been introduced in various aspects of life. Reinforcement learning is also a segment of machine learning that is based on the premise of automation.

What is Reinforcement Learning with MATLAB

Reinforcement learning is a kind of machine learning which enables a computer to function on its own by interacting with the dynamic environment repeatedly. The main aim of this approach is to reduce human intervention in machine learning and automation as much as possible so that a state of one hundred percent automatic technology can be attained.

Under reinforcement learning, the computer is not properly coded or programmed to perform the tasks but is made to act accordingly with the trial-and-error method. The environment or the outside conditions are made dynamic for the computer to explore as much as possible.

Applications like MATLAB enable this kind of function to run smoothly by providing schematic and organized results and outcomes. MATLAB is a professional tool that is fully documented and properly tested for carrying out functions like these.

With the help of MATLAB, reinforcement learning is done to get the best outcome suitable for a particular outside condition. All these functions are undertaken by a piece of software called the agent. The agent interacts with the outside conditions to produce various outcomes.

Understanding the Reinforcement Learning Workflow

To train the agent or a computer, the following steps are deployed:

  1. Creating the environment

The first step is to provide a suitable environment for the agent. The environment can be either a real-life condition or a simulation model. For technical and machine-based reinforcement learning, having a simulation model is preferred for smooth and safe functioning.

  1. Setting up a reward

A specific reward in the form of a numeric number has to be set up so that the agent can function accordingly. A reward is sometimes achieved by the agent after constant trials. Once the reward has been met, the optimal way to achieve the reward can also be found.

  1. Creating the agent

The agent for reinforcement learning can be created either by defining the policy representation or through the configuration of the learning algorithm of the agent.

  1. Training and validating the agent

For this, all the training options for the agent are set and training is started for the agent to tune the policy. In case an ideal validation of the agent has to be done, simulation turns out to be the option.

  1. Deployment of the policy

In the end, the policy representation is deployed using coding languages like MATLAB, etc.

A real-life example of reinforcement learning with MATLAB

Automated driving is the best example of machine learning, outcomes of which can be the result of reinforcement learning. The agent in the car uses various sensors to drive the car automatically without any human intervention. These sensors and video cameras give commands to the steering, gears, clutch, and brakes to take suitable action.

After a rigorous session of trial-and-error of various outcomes, the best way to automatically drive a car can be known. Reinforcement learning uses almost the same sort of applications while parking or reversing the car.

A shortcoming of reinforcement learning

Apart from its various benefits, reinforcement learning takes a lot of time and tries to achieve the optimal outcome.

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