Reinforcement Learning is a subfield of Machine Learning in which an agent explores an environment to learn how to perform specific tasks by taking actions with a good outcome and avoiding those with a bad one. A reinforcement learning model will learn from its experiences and will identify which actions lead to the best rewards.
In reinforcement learning, the agent takes action based on the state of the environment, and the environment will return the reward and the next state. The agent employs a trial and error method to learn. It initially takes random actions and identifies which actions lead to long-term rewards over time.
Applications of Reinforcement Learning:
Reinforcement Learning has applications in numerous fields – Self-driving cars, Robotics, Healthcare, Gaming, etc. Here are some of its real-world applications.
For self-driving cars, there are various aspects to consider, such as speed limits at different locations, driving zones, and collision avoidance .
Some ways in which Reinforcement Learning could be used in Self-Driving Cars include trajectory optimization, motion planning, including lane changing and parking, dynamically planning the most efficient path in a grid of potential routes, controller optimization, and scenario-based learning policies for highways.
AWS DeepRacer is an autonomous racing car designed for Reinforcement Learning testing on a physical track. It uses cameras to visualize the trajectory and a reinforcement learning model to control throttle and direction
Robots work in a highly dynamic and ever-changing environment, so it is difficult to predict what will happen next. Reinforcement learning provides a significant advantage in these situations because it makes robots robust enough to help adaptively acquire complex behaviors in different scenarios.
In the industry, learning-based robots are used to perform various tasks more efficiently and also those that are dangerous for humans. A great example is Deepmind’s use of AI agents to cool Google’s data centers which resulted in a 40% reduction in energy consumption. The centers are now fully controlled by an AI system without human intervention. There is still oversight from data center experts. The system works as follows:
- Taking snapshots of data from data centers every five minutes and feeding them to deep neural networks
- It then predicts how different combinations will affect future energy consumption.
- Identification of actions that will lead to minimum energy consumption while maintaining the established standard of safety criteria
- Dispatch and implement these actions in the data center
Reinforcement Learning can also train robots to grasp different objects, which can be used to build products on an assembly line. Google AI did an experiment in which seven real-world robots ran for 800 robot hours in 4 months and achieved a 96% grasping accuracy across 700 trial grasps on an object.
Automating Inventory Management is another excellent application of Reinforcement Learning. Using Computer Vision, the learning agent can locate an empty container and ensure that restocking is fully optimized.
In healthcare, Reinforcement Learning can be used in automated medical diagnosis, health resource scheduling, drug discovery and development, and health management.
Some of its uses include :
- Medical report generation, identification of nodules/tumors and blood vessel blockage, analysis of these reports, etc.
- Dynamic treatment regimes which involve sequential health care decisions such as the treatment type, dosage of the drug, and appointment timing. All these are tailored to the patient based on their medical history and condition over time.
- Robotic Surgeries to minimize errors and help increase the surgeon’s efficiency.
Traffic Light Control
Traffic congestion has become a significant problem, especially in metropolitan areas. Reinforcement Learning is a new data-driven approach for the adaptive control of traffic signals in complex urban traffic networks. These models are trained to learn a policy that optimally controls the traffic light based on the current traffic state.
Reinforcement learning is used in computer vision tasks such as image segmentation, object recognition, feature detection, and tracking.
Some other use cases of Reinforcement Learning in Image Processing include Scanners to read the text, Traffic analysis by frame-by-frame image segmentation, and Robots equipped with visual sensors to learn the state of the surrounding environment.
We needed a complex behavioral tree in traditional video games to create the game’s logic. However, using Reinforcement Learning, we can train the model much more simpler. The agent learns by itself in a simulated environment by performing necessary actions to achieve the desired behavior.
A great example of the application of Reinforcement Learning in the gaming frontier is AlphaGo Zero. Using reinforcement learning, AlphaGo Zero learned the game Go from scratch by playing against itself. After 40 days of self-training, it beat the strongest Go player in history, scoring a goal considered impossible at that time.
Based on customers’ behavior, a Reinforcement Learning model can give high-quality product recommendations to increase the company’s return on Investment and profit margins. The model will find which advertisements are more likely to be clicked, thereby increasing the customer footprint.
Trading and Finance
Supervised Learning algorithms can predict future stock prices; however, they do not determine the actions that must be taken at a particular instance. A Reinforcement Learning agent can decide whether to hold, buy or sell at any given price.
I am a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.