Introduction

Reinforcement learning is one of the fastest-emerging branches aiming to transform the scope of AI on real-world use cases. With its ability to adapt to the environment, reinforcement learning serves as a perfect partner to take hyper-personalization, advertising, trading, autonomous vehicle training etc. to a new level.

Hyper-personalization

One of the biggest drawbacks of the current recommender systems, especially in the News category, is that they keep on suggesting a similar kind of news to the reader; users can get bored. This generally happens because it only takes into account the current reward. However, with reinforcement learning, the recommender starts taking into account the future reward as well, which influences it to provide the reader with the perfect blend of similar and new items suited to their interest. In the research paper A Deep Reinforcement Learning Framework for News Recommendation, researchers discuss using the deep Q-learning-based approach, which delivered more promising results than the existing recommender systems.

 Autonomous vehicles training

In autonomous vehicles training, one of the biggest issues is that developers have to write a long list of customized rules to train the vehicle. Wavye is one of a few companies claiming to have developed a driverless car that trains with reinforcement learning. They don’t spend much time writing hand-made rules; instead, they just train the vehicle with the minimum number of rules. A human driver that was present in the car would intervene when an algorithm made a mistake. The algorithm was awarded the distance travelled without human intervention.

References

1. Reinforcement Learning – Applications in Business

Authored by Rhydham Gupta, Data Scientist at Absolutdata