Trends in Reinforcement Learning for Future

Trends in Reinforcement Learning for Future

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by Amelia Scott — 6 months ago in Development 2 min. read
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The industry is undergoing many transformations due to these trends in reinforcement learning. It is science and decision-making. It’s about learning how to be the best in a given environment and maximizing reward.

The best behavior can be learned by observing how the environment responds to your actions. This is similar to children learning from their environment and identifying the actions that will help them reach their goals.

The learner must discover by himself the sequence of actions that will maximize the reward, even if there is no supervisor. This process is similar to a trial and error search.

Not only is the immediate reward that they receive, but also any delayed rewards they may fetch, determines the quality of an action’s effectiveness. Reinforcement learning is a powerful algorithm because it can discover the actions that lead to success in an unknown environment.

Trends in Reinforcement Learning for Future

1. Reinforcement learning and the Intersection of ML & IoT

IoT, or the Internet of Things, is a technology that allows multiple devices (or “things”) to be connected over a network so they can communicate with one another. These devices are growing rapidly, to the point that by 2025 there could be 64 billion IoT devices. These devices gather data that can then be analysed and analyzed to gain useful insights.

Machine Learning is crucial here! Machine learning algorithms can be used for converting data from IoT devices to useful, actionable results that reinforce reinforcement learning.
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2. AI Engineering

Software engineering is well-known, but it is now AI Engineering that is gaining popularity as a profession. This is because the integration of RL into the industry has been very haphazard and ad-hoc without any best practices.

3. Automated feature engineering

Full AutoRL aims to create optimal models for new tasks with minimal human intervention and computation time. There are many decisions to make when building a machine-learning model. These include which architecture or algorithm to use, and how to set hyperparameters.

4. Neural Architecture Search

Expert-designed deep learning structures have shown remarkable performance in a variety of tasks, including image segmentation and language generation.
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5. Increased Use of AI in Cybersecurity Applications

RL-powered cybersecurity tools also can gather data from company’s transactional systems, websites, communication networks, and digital activity. They use RL algorithms to recognize patterns and identify threatening activity. This includes finding suspicious IP addresses, data breaches, and other possible threats.

Amelia Scott

Amelia is a content manager of The Next Tech. She also includes the characteristics of her log in a fun way so readers will know what to expect from her work.

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