Reinforcement Learning in Today’s World

Reinforcement Learning in Today’s World

Introduction

Reinforcement Learning is said to be the hope of Data science and artificial intelligence. And it is rightly said so, because the potential that Reinforcement Learning possesses is immense.Reinforcement Learning is growing rapidly, producing wide variety of learning algorithms for different applications.

What is Reinforcement Learning?

Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. Reinforcement Learning is learning what to do and how to map situations to actions. The end result is to maximize the numerical reward signal. The learner is not told which action to take, but instead must discover which action will yield the maximum reward.

For example : In usual situations we would require an autonomous vehicle to put safety first, minimize ride time, reduce pollution, offer passengers comfort and obey the rules while driving.With an autonomous race car, on the other hand, we would emphasize speed much more than the driver’s comfort. The programmer cannot predict everything that could happen on the road neither it knows it a very first place. Instead of building lengthy “if-then” instructions, the programmer prepares the reinforcement learning agent to be capable of learning from the system of rewards and penalties. The agent (viz.another name for reinforcement learning algorithms performing the task) gets rewards for reaching specific goals.

Challenges with Reinforcement Learning

The main challenge in reinforcement learning lays in preparing the simulation environment, which is highly dependent on the task to be performed. When it comes to building a model capable of driving an autonomous car, building a realistic simulator is crucial before letting the car ride on the street. The model has to figure out how to brake or avoid a collision in a safe environment, where sacrificing even a thousand
cars comes at a very minimal cost. Transferring the model out of the training environment and into to the real world is where things get tricky and tedious.

Reinforcement learning, as stated above employs a system of rewards and penalties to compel the computer to solve a problem by itself. Human involvement is limited to changing the environment and tweaking the system of rewards and penalties. As the computer maximizes the reward, it is much more prone to seeking unexpected ways of achieving it. Human involvement is focused on preventing it from exploiting the
system and motivating the machine to perform the task in an expected manner. Reinforcement learning is useful when there is no “proper way” to perform a task, yet there are rules the model has to follow to perform its duties in an appropriate manner.

Conclusion

A potential application of reinforcement learning in autonomous vehicles is the following interesting case. A developer is unable to predict all future road situations, so letting the model train itself with a system of penalties and rewards in a varied environment is possibly the most effective way.
Reinforcement learning is no doubt a cutting-edge technology that has the potential to transform our world. However, it need not be used in every case. Nevertheless, reinforcement learning seems to be the most likely way to make a machine creative as seeking new, innovative ways to perform its tasks is in fact creativity.
Thus, reinforcement learning has the potential to be a groundbreaking technology and the next step in Data science and AI development.

By: Yashi Sahu

 

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