Reinforcement learning involves training machine learning models to respond to certain stimulations in a variety of ways. Smother way to define reinforcement learning is that it is a special application of techniques from machine and deep learning that is designed to solve specific problems in a special way.
Artificial intelligence operates in a sort of game-like situation during reinforcement learning.
There is a series of trial and error phases before a solution is finally reached by the computer. To encourage perfection and actual response, the artificial intelligence is rewarded or awarded penalties as the goal in reinforcement learning is to maximize total reward. Also, because it is game-like, the operation has rules. In the rules or policy, the designer gives only clues but not the solution but allows the computer to solve the problem beginning from a random trial, to a strategic solution or artificial skill.
Notable, the use of search and trial and errors made reinforcement learning the most effective way to facilitate machine creativity. This is different from human intelligence artificial intelligence can gather data experience from several inputs which is sufficient to run on a computer.
In reinforcement learning, an agent is available which provides the rewards and penalties. This agent then is able to learn from the errors.
Is there example of reinforcement learning?
A notable experimented was tried in reinforcement learning in 1992 by Gerald Tesauro at IBM’s Research Center. This program was called TD-Gammon which was a computer backgammon (a historical board game dates back to about 5000 years ago). This name was from an artificial neural network trained from temporal-difference learning. What happened in 1992, has taken a new turn with the presence of powerful computational tech. these technologies have open ways fr new and fantastic applications to aid reinforcement learning.
Before example, we may need to know that in designing autonomous vehicle, safety should be first, followed by speed, no pollution and comfort for passengers and traffic rules. But with an autonomous car designed for racing, speed would be emphasized. Hence, we cannot predict all that would happen on the road. Rather than building algorithm based on speculations such as “if-then pack or if-then reduce speed by 10km/hr.” the programmer then equips the reinforcement learning agent to be able to learn from the system of rewards and penalties. In this sense, the agent which is the algorithm gets the rewards for desired outcome.
Problems faced by reinforcement learning
Simulation environment: This is often seen as the main challenged faced by reinforcement learning. Simulation environment is dependent on the task to be performed. For example, if a model is building probably a super intelligent chess master game, the simulation environment is simple. But to build an autonomous car or aero plane is tough. This is because, designing a realistic stimulator is important before allowing an autonomous car move in the street. Feedback in this case, feedback is necessary to monitor excesses of the car.
Scaling the neural network: Tweaking and scaling the neural network controlling agent is a problem in the sense that, communication within networks is through reward and penalties. Given this order of communication, there is a likelihood of forgetfulness where stored memories may be erased to acquire new ones.
Don’t forget, reinforcement learning is allowing computer to develop while learning from its errors through agent’s rewards and penalties. The question is, would be reach the perfection stage? Well, research studies in this field are still open and very much unlimited.