- Insights
- Cryptocurrencies
- Stocks
- White Papers
- Industry
- Geography
- Insights
- Cryptocurrencies
- Stocks
- White Papers
- Industry
- Geography
Artificial Intelligence (AI) has had the power of ruling the technologically dominated world for quite some time now. Today, we have reached a stage wherein advanced artificial intelligence has become one of the most sought after techniques to bring about innovation and solve complex business problems. Over the last few years, the technology has matured to the extent that it has become highly scalable. In the midst of all this, what has grabbed eyeballs from everywhere across is reinforcement learning – training the machine learning models to be able to make the best possible decisions.
Reinforcement learning makes use of algorithms that do not rely only on historical data sets, to learn to make a prediction or perform a task. Just like we humans learn using trial and error, these algorithms also do the same.
In addition to accelerating as well as improving the design, reinforcement learning has grabbed attention for its wide application in a range of areas – time-series forecasting in highly dynamic conditions, solving complex logistics problems, and coming up with recommendations based on the behaviours and preferences, to name a few.
One of the most remarkable features of reinforcement learning is that the AI agent need not be explicitly programmed. This is by virtue of its ability to learn from examples and teach itself through the trial and error method. What follows is a novel and adaptive solution, oftentimes faster than humans could do so.
As the AI agent learns through trial and error, what actually happens is that the agent performs actions within an environment and receives rewards when it takes the "right" actions. Simply put, the AI agent aims to find the sequence of actions that maximize the cumulative rewards it receives. The reward function for the agent is defined by the data scientists and subject-matter experts.
Reinforcement learning falls into three categories: speeding design and product development, optimizing complex operations, and guiding customer interactions.
Reinforcement learning stands the potential to improve the development of products, engineering systems, manufacturing plants, oil refineries, telecommunications or utility networks, and other capital projects. With this, the mining companies are now in a position to explore a greater range of mine designs than possible with the other AI techniques used today to improve yield.
With a majority of organizations dealing with complex operations, reinforcement learning turns out to be no less than a saviour for them. Reinforcement learning's ability to solve complex problems makes it easier than ever for organizations. In addition to helping the organizations identify the right actions to take across a value chain as events unfold, reinforcement learning can help teams manage complex manufacturing processes.
Understanding, identifying and responding to the changes in taste in real-time, followed by personalizing the messages and coming up with promotions, offers, and recommendations daily have got a lot easier by virtue of reinforcement learning.
I order to build a successful reinforcement learning system, equal and considerate attention in three areas, namely – a well-designed learning algorithm with a reward function, apt learning environment and faster computing power is required.
Taking into account the magic that reinforcement learning unfolds, a few years from now, it is anticipated that reinforcement learning would become more common in many industries, such as telecom, pharmaceuticals, and advanced industries.
