Reinforcement Learning and Its Practical Applications

Reinforcement Learning and Its Practical Applications


Reinforcement learning is a type of Machine Learning algorithm which allows software agents and machines to automatically determine the ideal behavior within a specific context, to maximize its performance.

Some of the practical applications of reinforcement learning are:

1. Manufacturing

In Fanuc, a robot uses deep reinforcement learning to pick a device from one box and putting it in a container. Whether it succeeds or fails, it memorizes the object and gains knowledge and train’s itself to do this job with great speed and precision.

Many warehousing facilities used by eCommerce sites and other supermarkets use these intelligent robots for sorting their millions of products everyday and helping to deliver the right products to the right people. If you look at Tesla’s factory, it comprises of more than 160 robots that do major part of work on its cars to reduce the risk of any defect.

2. Inventory Management

A major issue in supply chain inventory management is the coordination of inventory policies adopted by different supply chain actors, such as suppliers, manufacturers, distributors, so as to smooth material flow and minimize costs while responsively meeting customer demand.

Reinforcement learning algorithms can be built to reduce transit time for stocking as well as retrieving products in the warehouse for optimizing space utilization and warehouse operations.

3. Delivery Management

Reinforcement learning is used to solve the problem of Split Delivery Vehicle Routing. Q-learning is used to serve appropriate customers with just one vehicle.

4. Power Systems

Reinforcement Learning and optimization techniques are utilized to assess the security of the electric power systems and to enhance Microgrid performance. Adaptive learning methods are employed 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 to reduce transmission losses and CO2 emissions.

Applications of Reinforcement Learning are highlighted for three research problems in power systems.

First, Reinforcement Learning is used to develop distributed control structure for a set of distributed generation sources. The exchange of information between these sources is governed by a communication graph topology.

Second, an online adaptive 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. Only partial knowledge about the Microgrid’s dynamics is required.

Finally, Q-Learning with eligibility traces technique is adopted to solve the power systems non-convex Economic Dispatch problem with valve point loading effects, multiple fuel options, and power transmission losses. The eligibility traces are used to speed up the Q-Learning process.

5. Finance Sector

Pit.AI is at the forefront leveraging reinforcement learning for evaluating trading strategies. It is turning out to be a robust tool for training systems to optimize financial objectives. It has immense applications in stock market trading where Q-Learning algorithm is able to learn an optimal trading strategy with one simple instruction; maximize the value of our portofolio.

This way anyone who is able to get his/her hands on a Q-Learning algorithm will potentially be able to gain income with worrying about the market price or the risks involved since the Q-Learning algorithm is smart to take all these under considerations while making a trade.


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