Adjoining reinforcement learning and deep learning form the new term Deep Reinforcement Learning (RL). Deep reinforcement learning is a subpart of machine learning (ML) and artificial intelligence (AI), where intelligent machines learn from their actions just the way humans learn from their past experiences. The DRL (deep reinforcement learning) algorithm works on a trial and error basis.
There are two different forms of deep reinforcement learning – supervised and unsupervised ML. For supervised learning, a machine calculates the label to use for complex inputs, while for unsupervised learning, a system of group-related items is used to improve outcomes. Reinforcement learning can help to achieve the best possible results as it possesses the capability of predicting actions.
Working of deep reinforcement learning
A continuous form of data is fed into the machine. Unlike other ordinary ML-enabled systems, this type of learning is reinforced. The reinforcement learning process (RL) involves training software agents to learn to carry ideal behavior within the particular environment to help achieve optimized results.
While undergoing reinforcement learning, an agent is rewarded for any positive behavior (to encourage such actions) and punished for any negative behavior (to discourage such action). Ultimately, an agent can learn the desired behavior that maximizes the total reward. Such a pattern of deep reinforcement learning makes it most applicable in dynamic environments.
The concept of reinforcement learning came into existence even before AI came into force. Its combination with deep learning paved the way for tech experts to achieve excellent results. In the term deep reinforcement learning, the word “deep” refers to several layers of artificial neural network replicating the human brain’s structure. Moreover, it is a fast-moving field applied in various spheres to escalate processes and maximize outputs.
How deep reinforcement learning benefits businesses?
Let’s learn how deep reinforcement learning can prove helpful for businesses in different fields –
Robots in factory
Consider the task of boxing a product and adding it into a larger container. Robots can perform this task with great speed and accuracy by training themselves. Here, robots use deep reinforcement learning, where they are trained to learn and perform a new task.
While performing a particular task, robots make sure to capture the video footage of the process. Here whether the tasks succeed or fail, it memorizes the action and acquires knowledge as part of the deep learning model controlling the robot’s actions.
A Japanese company named Fanuc created an industrial robot that is intelligent enough to train itself to perform a particular task.
Optimization of space management in warehouses
Warehouse managers often face challenges in looking for the best solutions while optimizing space utilization. The abundant amount of inventory, fluctuating demands for stocks, and slow replenishing merchandise rates are factors that a manager needs to take care of before accumulating items in a warehouse. The reinforcement learning algorithms help reduce transit time for stocking and retrieving products in the warehouse, even optimizing space utilization and warehouse operations.
Adjusting pricing depending on supply and demand helps maximize revenue from possible products by using dynamic pricing. A technique called Q-learning techniques is used to find a solution for a dynamic pricing problem. Reinforcement learning algorithms help businesses to optimize pricing during interactions with customers.
A manufacturer who wants to deliver products for customers using a fleet of trucks is set to serve customer demand. The manufacturer can use Split Delivery Vehicle Routing Problem to make split deliveries and realize savings in the process. The manufacturer’s prime objective is to reduce total fleet cost while meeting all demands of the customers.
Introducing a multi-agent system helps agents to communicate and co-operate with one another through reinforcement learning. The advantage of using Q-learning to serve appropriate customers with just one vehicle proves helpful for the manufacturer. The manufacturer reaps benefits by improving execution time and reducing the number of trucks used to meet customers’ demands.
Personalization is the need of the time, especially when it comes to the shopping experience. Retailers and e-commerce merchants enhance customer purchasing habits and have shown an absolute imperative to map communications and promotions through personalization.
Personalization in such cases helps retailers to promote relevant shopping experiences to attain customer attention. E-commerce merchants can learn and analyze customer behaviors and tailor products and services to suit customer interests using reinforcement learning algorithms.
In the medical industry
As a medical research subject, a dynamic treatment regime (DTR) is used for setting rules in finding effective treatments for patients. An illness like cancer demands treatment for a longer period where drugs and treatment levels need to be administered for a particular time.
Reinforcement learning helps address the DTR problem where RI algorithms can process clinical data to develop a treatment strategy, using various clinical indicators collected from patients as inputs.
Humans are searching for different ways to make the machine perform human tasks, and emerging technologies have even made it possible. Even though there is a significant difference between the idea and reality, reinforcement learning has driven hope by making robots and machines perform tasks that were not possible at one time.
Considering it just the beginning, it plays a major part as an innovative technology that can drive business value.
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