Experience Extended | Potential Applications of Reinforcement Learning 2021-02-09T14:42:42+00:00

Experience Extended | Potential Applications of Reinforcement Learning

Reinforcement learning is a branch of machine learning wherein actions are taken to maximize a cumulative reward based on interactions with an environment. Because of the ability of reinforcement learning to learn on the fly and auto-correct, it is finding applications in a variety of fields and showing greater benefits than traditional machine learning algorithms.

Reinforcement Learning in Industry Automation

Reinforcement learning can be used in industries where complex decisions are needed to be made in dynamic environments.[1] Manufacturing companies with strict production schedules demand automated machines so that they can optimize production times, product quality, and cost. Reinforcement learning can help these machines learn from the past and improve previous mistakes. The more the robot performing reinforcement learning interacts with this environment, the more it learns about the process and recommends best practices to further improve its automation. Car manufacturing companies are leveraging these RL-based robots to reduce defects when building and assembling car parts.

Reinforcement Learning in Finance

Time series modeling is often used to forecast stock prices. However, to decide whether to buy, sell or hold the stock is where reinforcement learning comes into picture.[2]. Portfolio optimization – where the goal is to create an optimal portfolio with specific factors that need to be maximized or minimized – can be done using various reinforcement learning algorithms like Deep Q-Learning, policy gradient, PPO, and A3C. Reinforcement learning can also make financial trades where the reward function can be computed based on the profit or loss of every financial transaction.

Reinforcement Learning in Marketing

Owing to the ability of reinforcement learning to self-learn and self-correct on the fly, it can find various applications in marketing. Reinforcement learning can dynamically ingest consumer preferences and behaviors and provide high-quality recommendations in line with them. Even though this sounds similar to a traditional recommendation engine, the bonus here is that the algorithm can learn real-time and adjust the recommendations based on the success (like number of clicks or visits) that these recommendations are bringing. [3]

Reinforcement Learning in Advertising

Traditionally, A/B testing is used to select the best content for advertising. Since A/B testing is static and one has to wait till the end to learn the results, reinforcement learning (which learns dynamically) can find the most optimal content much faster. This can significantly reduce the number of times non-optimal content is shown, which consequently maximizes revenue.

References

  1. Reinforcement Learning Applications
  2. Reinforcement Learning Applications in Finance
  3. Revolutionize Digital Marketing Case Studies
Authored by Nibedita Dutta, Data Scientist at Absolutdata