What is Reinforcement Learning? The intuition behind it Reinforcement learning (RL) is an “ancient” area of ...
Reinforcement learning (RL) is an “ancient” area of machine learning that recently gained a lot of attention thanks to new discoveries by google.
The objective of Reinforcement Learning is to take sequential decisions in an optimal way. More specifically an RL algorithm takes short-term decisions while optimising for a longer-term goal through trial and error.
Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.
In this guide you will learn:
Reinforcement Learning has recently made the headlines as computer programs ALphaGgo and ALphaZero prove to be breakthrough technologies. Developed by DeepMind Technologies, the now google owned computer program AlphaGo made history as the first computer program to defeat a professional human in the most challenging board game for AI – the abstract strategy game called Go.
Reinforcement Learning isn’t limited to merely beating you in games like chess however. Many aspects of our lives have already been revolutionized by the concept. These include:
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Reinforcement Learning can create value for organisations that deal with complex problems.
In particular, organisations managing complex workflows involving people, machinery and other variables that cannot be controlled (e.g. Markets, weather, road traffic, etc.) are the ones who can benefit the most from it.
The reason is simple and it relates to the very nature of Reinforcement Learning : it continuously learns over time by receiving rewards and punishments for every action taken.
This interesting property enables Reinforcement Learning to react to events or environments that it has never seen before.
More specifically, it is particularly suited for :
Reinforcement learning brings several upsides when implemented within an organisation. The three most important being:
Reinforcement learning can maintain a balance between the two. Exploitation seeks faster results at the expense of better results. Exploration does take far longer, but produces far superior results in general because of its randomness. Reinforcement learning can solve the problem of slow learning in ML with a balance between exploration and exploitation. Resulting in greater performance and efficiency compared to other algorithms.
Many believe deep learning to be one of the biggest breakthroughs in the history of AI. That doesn’t mean it’s going to solve all our problems. We still have countless challenges ahead of us.
However, we must admit that the basic idea is fascinating: a computer program iteratively improves its performance on a task by trying different actions and learning from the resulting feedback.
Reinforcement learning is the AI cherry. It’s something that we haven’t had yet, but it’s been a long time coming and it will forever change the way we build software.