Reinforcement Learning

Reinforcement Learning

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Reinforcement learning is a groundbreaking technology in the AI development. It provides the optimum environment for a machine to be creative – finding new and innovative ways to solve problems and perform its tasks.


A Machine Learning subfield, Reinforcement Learning (RL) is concerned with decision making and motor control. It is designed to train ML models make a sequence of decisions through interaction with the environment. Employing a system of rewards and penalties to compel the computer to solve a problem by itself. The computer employs trial and error to come up with a solution to the problem. As the computer maximizes the reward, it is prone to seeking unexpected ways of doing it.

Solve business challenges and optimise results with intelligent solutions through Reinforcement Learning.

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    It is one of the core technology that enabled self-driving cars, automated industrial systems, robotics and even dynamic treatment regimen in healthcare.

    Real-world Applications of Reinforcement Learning


    Patients can now receive treatment from policies learned from RL systems. RL application in healthcare is categorised as dynamic treatment regimes (DTRs). It is applied in chronic disease, critical care as well as automated medical diagnosis.

    Application of RL in DTRs is advantageous because of its capability to determine time-dependent decisions for the best treatment for a patient at a specific time. It also enables improvement of long-term outcomes by factoring the delayed effects of treatments.

    Reinforcement Learning

    Trade and Finance

    Supervised learning models are used to predict future sales and to predict stock prices however these models are not able to determine the action to take for a particular stock. Comes RL, the model can decide whether to buy, sell or hold that stock, with rewards based on the profit or loss of each financial transaction. Today, large tech organisations have sophisticated models with the ability to make financial trades.

    Industrial Automation and Robotics

    RL in industrial applications and robotics to gain the skills themselves for performing their tasks. It can train robots and use them in building products in the assembly line.

    Resource Management System

    Having limited resources can be a challenge. Through RL, distribute limited resources to the activities and departments that has the highest potential for an organisation to reach the goal – either in the shortest possible time or with the highest gain.

    Traffic Control System

    Application in managing traffic is a demonstration of RL’s usage in real-time decision-making and optimisation for traffic control activities.

    What’s Your Challenge?

    Speak to AI Consulting Group’s reinforcement learning specialist to discuss how you can optimise and create intelligent agents to solve your business challenges.

    We have core capabilities across the following domains:

    Machine Learning Image RecognitionVoice Analytics Video Analytics Text & Sentiment Analysis Natural Language Processing Reinforcement Learning Digital Twins Autonomous DecisionsStream AnalyticsAutonomous Control Unsupervised Learning Reporting KPI Monitoring

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