Reinforcement Learning

This year, we have seen all the hype around AI Deep Learning. With recent innovations, deep learning demonstrated its usefulness in performing tasks such as image recognition, voice recognition, price forecasting, across many industries.

It’s easy to overestimate deep learning’s capabilities and pretend it’s the magic bullet that will allow AI to obtain General Intelligence. In truth, we are still far away from that. However, deep learning has a relatively unknown partner: Reinforcement Learning. As AI researchers venture into the areas of Meta-Learning, attempting to give AI learning capabilities, in conjunction with deep learning, reinforcement learning will play a crucial role.

What is Reinforcement Learning?

Imagine a child who is learning by interacting with their environment. Each touch will generate a sensation that can result in a reward. For instance, the pleasant smell of the flower will entice the child to want to smell the flower again; the pain from a prick of the flower’s stem will alert the child who will refrain from touching the stem again.

In each case, as the child interacts with the environment, the environment reciprocates and teaches the child by rewarding the child with different sensations.

The child is learning by trial and error.

This is reinforcement learning. In reinforcement learning, an agent starts in a neutral state. Then, as actions are taken, the environment helps the agent transition from the neutral state to other states. In these other states, there might be rewards for the agent.

The goal of the agent is to gather as many rewards as possible.

You can visualize yourself as an agent, walking on a reinforcement learning path, starting at the beginning of a maze toward the exit. With each step that you take, you have a chance of collecting rewards that you can tally up. Depending on the type of rewards and the quantity of the rewards, in your reward pouch, decisions can be made to direct you toward the exit. Eventually, with many tries, an optimal path can be found through the maze.

…………………………………………….

Jun Wu

Jun Wu

Read more on Forbes

 

You May Also Like

Leave a Reply

Your email address will not be published. Required fields are marked *