Reinforcement Learning for Real-Life Apps

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Reinforcement Learning for Real-Life Apps

There is a lot of research using Reinforcement Learning in many different areas. Read about 10 ways how Reinforcement Learning is being utilized to solve real-world problems in various industries.

From healthcare, and finance to gaming or robotics – the potential of Reinforcement Learning (RL) to improve decision-making and optimize outcomes is enormous. In a recent article, describe 10 use cases, that show the potential of this cutting-edge technology:

1. Self-driving cars Deep Reinforcement Learning is used for various tasks in self-driving cars, such as making decisions on how to act in dangerous situations, trajectory optimization, motion planning, or dynamic pathing.

2. Industry Automation In industry, AI-controlled Reinforcement Learning robots help fulfill tasks more efficiently. For example, in Google data centers the result is a 40 % reduction in energy spending.

3. Trading and Finance In trading and finance Reinforcement Learning is being applied to make decisions such as buying, selling, or holding stocks. Supervised time series models can predict stock prices, but RL can determine the best action.

4. NLP (Natural Language Processing) In Natural Language Processing, Reinforcement Learning is used for tasks like text summarization, question answering, and machine translation. An RL-based approach for question answering is proposed in this paper by Eunsol Choi, Daniel Hewlett, and Jakob Uszkoreit, which uses a slow RNN to select and answer relevant sentences.

5. Healthcare Reinforcement Learning can find the best treatment options for patients by learning from previous experiences. It can also improve long-term healthcare outcomes by considering treatments' delayed effects.

6. Engineering As an example, Facebook has developed an open-source reinforcement learning platform called Horizon, which optimizes large-scale production systems in the engineering field. Horizon has been used internally by Facebook for tasks such as personalizing suggestions, delivering more meaningful notifications, and optimizing video streaming quality.

7. News Recommendation In news recommendations, Reinforcement Learning is being used to track user preferences which can change frequently. This approach involves obtaining user behaviors and combinations with news and context features.

8. Gaming One great example of Reinforcement Learning in gaming is AlphaGo Zero which learned the game "Go" within 40 days while playing against itself. It was even able to beat the world's number-one player.

9. Marketing and Advertising In marketing and advertising, Reinforcement learning can help optimize ad placement and targeting by learning from the past performance of different strategies. It can also derive optimized pricing strategies from consumer buying behavior.

10. Robotics Manipulation Last but not least, Reinforcement Learning is used for training robots in the ability to autonomously grasp various objects. This can be very beneficial in for example production or logistics.

If you would like to read about these applications in detail, find the whole article here:

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