Reinforcement learning (RL) can be applied to a wide range of real-world use cases. We give some examples next.
Marketing personalization
In applications like recommendation systems, RL can customize suggestions to individual users based on their interactions. This leads to more personalized experiences. For example, an application may display ads to a user based on some demographic information. With each ad interaction, the application learns which ads to display to the user to optimize product sales.
Optimization challenges
Traditional optimization methods solve problems by evaluating and comparing possible solutions based on certain criteria. In contrast, RL introduces learning from interactions to find the best or close-to-best solutions over time.
For example, a cloud spend optimizing system uses RL to adjust to fluctuating resource needs and choose optimal instance types, quantities, and configurations. It makes decisions based on factors like current and available cloud infrastructure, spending, and utilization.
Financial predictions
The dynamics of financial markets are complex, with statistical properties that change over time. RL algorithms can optimize long-term returns by considering transaction costs and adapting to market shifts.
For instance, an algorithm could observe the rules and patterns of the stock market before it tests actions and records associated rewards. It dynamically creates a value function and develops a strategy to maximize profits.