Artificial intelligence is changing how we interact with the world around us. Moreover, it is bringing many changes to the world that will significantly affect our lives. One such thing poised to take AI systems to the next level is reinforced learning (RL). It refers to the ability of AI agents to learn through trial and error. To make things simple, let’s first learn the basics of RL. There are a few components of RL that make it different from traditional AI LLMs.
The first is the AI agent. So, an AI agent is an AI system that interacts with the environment around it. Next is the environment, which is the external system or environment where the AI system works or interacts. Rewards, penalties, and policies are other components of RL. To make things simple, let’s say the AI agent is a toddler, and the world is its environment. Let’s say the toddler is learning what to do if a dog chases it. Here, the dog is the environment. If the toddler doesn’t do anything and the dog bites it, that’s a penalty. On the other hand, if the toddler understands that it has to run to get rid of the dog, then this is its reward. The policy is that the toddler knows you have to outrun the dog to stay safe or get the reward.
This is the basis of RL, which is why it is one of the best ways to train AI systems based on tried and tested methods. Another example is the auto-drive feature in cars like Tesla. Here, machine learning is done through RL in various potential scenarios. Because of this, now the car knows what to do in specific scenarios, whether to brake or accelerate. As the CEO of one of the world’s leading AI firms, DeepMind, Demis Hassabis, says, “Reinforcement learning allows us to create intelligent agents that can learn to achieve goals in complex, uncertain environments.” Moreover, RL enables AI agents to make decisions for the scenarios they have never been trained for.
Many use cases of reinforcement learning exist, and tech companies worldwide are testing them extensively. The first application of RL AI agents comes from self-driving cars. Car manufacturers extensively use RL to ensure the car is ready for any scenario. Another application of RL is in robotics. This is evident in the robot made by one of the world’s leading robotics companies, Boston Robotics, which used RL in their ‘Spot’, which can navigate various terrains without human input. The impact of RL can also be seen in healthcare, particularly in the field of surgery. One of Elon Musk’s companies, Neuralink, has a robot that uses RL to operate precisely. The future of RL is bright, and it’s just a matter of time before pretty much every industry starts utilising it in its operations.
