In today’s world machines can teach themselves based upon the results of their own actions. This advancement in Artificial Intelligence promise the technology through which we can explore more innovative potentials of AI. The process is termed as Reinforcement Learning.
What is Reinforcement Learning? Let’s imagine a new born baby comes across a lit candle. Now, the baby does not know what happens if it touches the flame. Eventually, out of curiosity, the baby tries to touch the flame and gets hurt. After this incident the baby will learn that repeating the same thing again might get him hurt. So, the next time when it sees a burning candle, it will be more cautious.
That is what exactly how Reinforcement learning works. Reinforcement learning is a kind of Machine Learning system where the system is to be trained to do a particular job, learns on it’s own based on its previous experiences and outcomes while doing a similar kind of a job.
Look at the image here.
In the image of an apple and ask it to identify it.
The computer came up with an answer as you can see on the image…it says it’s a ‘mango’.
You tell the system that it’s a wrong answer and the image was an apple. That’s the feedback.
The machine learns from the feedback.
Finally, if it come across with another image of an apple, it will be able to identify it correctly. That’s reinforcement learning.
So, in the case of reinforcement learning, the system will takes a decision, learns from the feedback and takes better decisions in the future.
Reinforcement learning is a powerful machine learning technique, in which it lead to a functional AGI system is debatable. RL is a artificial intelligence technology that can be used to learn strategies to optimally control large, complex systems such as manufacturing plants, traffic control systems (road/train/aircraft), financial portfolios, robots, etc. Right now, it transitioning from research labs to highly impactful, real world applications. For example, self-driving car companies are like Wayve and Waymo using reinforcement learning to develop the control systems for their cars.
Artificial Intelligence systems that are typically used in industry perform pattern recognition to make a prediction. For an instance, they might to recognize the patterns in images to detect faces (face detection), or recognize patterns in sales data to predict a change in demand (demand forecasting), and so on. On the other hand, Reinforcement learning methods, are used to make optimal decisions or take optimal actions in applications where there is a feedback loop.
Many of the recent, prominent demonstrations of RL come from applying them to board games and video games. The first Reinforcement learning system to impress the global AI community want to learn the outplay humans in different Atari games when only given as input the images on screen and the scores received by playing the game.
The Deepmind and the AI research lab “OpenAI” have released systems for playing the video games Starcraft and DOTA 2 that can defeat the top human players around the world. These type of games are challenging because they require strategic thinking, resource management, and control and coordination of multiple entities within the game.
The technology is advanced with leaps and bounds that determined to progress to do great things in the near future. Experts believe that reinforcement learning is at the cutting-edge right now and it has finally reached a to be applied in real-world applications. The future of Reinforcement Learning in the real world does seem very bright. There are many startups offering reinforcement learning products for controlling manufacturing the robots (Covariant, Osaro, Luffy), managing the production schedules (Instadeep), enterprise decision making (Secondmind), logistics (Dorabot), circuit design (Instadeep), controlling autonomous cars (Wayve, Waymo, Five AI), controlling drones (Amazon), running the hedge funds (Piit.ai), and many other applications that are beyond the reach of AI recognized pattern systems.
The advancements made by Reinforcement Learning, one might easily say that it can very well form the future of Machine learning. As of now, robots are coming in to the larger picture of Technology and as they grow bigger, so will reinforcement learning.
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