Deep Reinforcement Learning and Its Applications

 In Our Thinking

The term Deep Reinforcement Learning is a new cool phrase in the world of Artificial Intelligence and Machine Learning. So, what does this phrase mean, and what is its impact?

What is Deep Reinforcement Learning?

Deep Reinforcement Learning uses the combined principles of deep learning and reinforcement learning.

Deep Learning, as we know, Deep learning is a part of machine learning methods and is based on artificial neural networks.

Reinforcement Learning, on the other hand, is an area of machine learning which tells how software agents should take actions to maximize the probability of choosing the best possible path or behavior for a particular situation.

The combination of these principles results in creating efficient algorithms that are applied in various fields such as robotics, healthcare, etc. It helps in scaling up the efficiency of normal routine machine learning models and helps solve previously unsolvable problems.

How Does Deep Reinforcement Learning Work?

Now the next question which comes to mind is, how does Deep Reinforcement Learning work? Well, it takes the best actions from what we see and hear.

Reinforcement learning is goal-oriented algorithms, which learn to reach a complex goal or maximize the probability of a decision along a particular dimension over many steps. For example, how to maximize the points won in a game over many moves? They can start from a blank page, and under proper conditions, achieve superhuman performance. These algorithms are penalized when they make a wrong decision and awarded when the decision is right.

Reinforcement learning analyzes actions by the produced results. Its goal is to learn sequences of actions that lead an agent to achieve its goal or maximize its objective function.

Let us look at some examples. In video games, the goal is to finish the game with the most points, so each additional point scored throughout the game will affect the agent’s subsequent behavior – the agent may learn that it should shoot, touch coins, or dodge starts to maximize its score. In the real world, the goal might be for a robot or automated vehicle to move from source A to destination B, and every inch the robot or vehicle can move closer to destination B could be counted as points.

Real-World Applications of Deep Reinforcement Learning

Let us take a look at some of the practical applications of Deep Reinforcement Learning to understand this concept better –

1.     Video Games: Deep Reinforcement Learning is used to make complex interactive video games where the Reinforcement Learning agent’s behavior changes based on its learning from the game to maximize the score. It is widely being used in PC games like Assasin’s Creed, Chess, etc. wherein the enemies change their moves and approach based on the player’s performance.

2.     Inventory Management: One of the major problems in supply chain inventory management is the coordination of policies adopted by various agents of inventory management such as suppliers, distributors, manufacturers to have a smooth material flow, minimize costs, and meet customer demand. Reinforcement learning algorithms can be helpful here in building complex models to reduce transit time for stocking as well as receiving products in the warehouse leading to optimized space utilization and warehouse operations. 

3.     Manufacturing: Many manufacturing companies like Fanuc use deep reinforcement learning in robots to pick a device from one box and put it in a container. It memorizes the object and gains knowledge and trains itself to do this job with high speed and accuracy. The warehousing facilities of several eCommerce websites and also supermarkets use these super-smart robots for sorting millions of their products every day and help deliver the right product to the right customer. Tesla’s factory comprises more than 160 robots that do a significant part of work on its cars to reduce the possibility of human error.

4.     Power Systems: The security of electric power systems and Microgrid performance enhancement is done using a combination of reinforcement learning and optimization techniques. Reinforcement Learning is used for Adaptive learning methods to develop control and protection schemes. Transmission technologies with High-Voltage Direct Current (HVDC) and Flexible Alternating Current Transmission System devices (FACTS) based on adaptive learning techniques can effectively help in reducing transmission losses and CO2 emissions.

Reinforcement Learning is applied in three research problems in power systems. First, it is used to develop a distributed control structure for a set of distributed generation sources. A communication graph topology supervises the exchange of information between these sources. Second, an online Reinforcement Learning technique is used to control the voltage level of an autonomous Microgrid. The control strategy is robust against any disturbances in the states and load. Finally, Q-Learning, with eligibility traces technique, is adopted to solve the power systems problems of multiple fuel options and power transmission losses. The eligibility traces help in improving the efficiency and speed of the Q-Learning process.

5.     Finance Sector: leverages reinforcement learning to evaluate trading strategies. It is a robust tool for training systems to optimize financial objectives. It has a huge application in stock market trading where the algorithm can learn an optimal trading strategy by maximizing the value of trading portfolios.

Industry experts are coming up with new and smarter ways of leveraging deep reinforcement learning every day. The most promising future of reinforcement learning is to create agents to make smarter decisions without human intervention and to make better decisions than humans and with more accuracy.

Most of us interact with AI almost every day. We trust the translations made by Google, Bing, and Facebook. Going forward, reinforcement learning, used to make smarter decisions by agent’s self-learning techniques. The future looks promising, and we couldn’t be more excited.

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