How is Research in Reinforcement Learning (RL) Changing the World around Us?

In the recent decade, Artificial Intelligence (AI) has made noteworthy contributions ranging from speech recognition, self-driving cars to a humanoid robot. Today, Artificial Intelligence has become the talk of the town and has given scope to several debates. While some call AI as ‘cognitive computing’, others rebrand it as ‘machine intelligence’.  

There is no iota of doubt that each area (network with memory, generative models, etc.) of Artificial Intelligence has impacted the world around us. But when compared to other major AI areas, it is Reinforcement Learning (RL) that has taken the technology world by storm.

Reinforcement Learning, also known as adaptive dynamic programming (ADP), is considered to be a robust tool for solving complex decision-making theories. It is a paradigm for learning through trial & error approach and allows machines to determine the specific behavior to maximize its performance. 

Some of the popular contributions of reinforcement learning include:

• Scalable deep RL with importance weighted actor-learner architecture – The core idea behind this project is to develop a scalable and fast policy gradient agent, IMPALA (importance weighted actor-learner architecture) to collect experience that is passed to the central learner. IMPALA can be incorporated either using single or multiple-learners conducting synchronous updates. The key benefit of this study is high data throughput rates can be achieved efficiently. 
• Model-free deep RL for model-based control – Model-free reinforcement learning algorithms have the potential to achieve the asymptotic performance (but are not efficient). On the other hand, model-based reinforcement learning algorithms best for higher asymptotic bias and are efficient. Here, the major idea is to combine the benefits of model-based & model-free reinforcement learning and accomplish asymptotic performance that is close to model-free algorithms. 
• Hierarchical imitation & reinforcement learning – This project introduces a hierarchical guidance framework that combines reinforcement learning & imitation learning (IL) to find solutions to problems that can be segregated into subtasks. The algorithm framework leveraging the hierarchical structure of issues such as labeling high-level trajectory with suitable macro-corrections, ignoring the sub policy if the macro-action is incorrect. The final outcome of the study implied that hierarchical imitation learning needs fewer labels than standard ones. 
• Unsupervised predictive memory in a goal-oriented agent – To overcome the issue of problem partial observability, a new model known as memory, RL, and interference network (MERLIN). This model offers a new approach to incorporate memory into the model. The major idea here is to segregate MERLIN into 2 components known as a memory-based predictor (MBP) and policy network which receives state variables. The outcome of the study concludes that the combination of predictive modeling and memory improves the performance of RL agents. 

With its growing importance, reinforcement learning is finding its applications in various sectors. 

 1. Manufacturing industry:- In the manufacturing sector, reinforcement learning can be used to pick devices and put them in a container with precision and at higher speeds. This is accomplished by memorizing objects and gaining knowledge. This technique, along with robots can also be used by the eCommerce businesses to sort out the products and deliver them to the customers. 

2. Power systems:- Optimisation and reinforcement learning techniques can be utilized to evaluate the security of electric power systems and improve the performance of microgrid. Adaptive learning approaches can be employed to create control & protection devices. One of the advantages of using RL technique is that it lets you develop a controlled structure for distributed generation sources, governs the communication topology graph, and controls the voltage level of an automatic microgrid. 

3. Finance industry:- Today, RL is widely used in the banking sector for training systems to maximize and optimize the financial objectives. Also, trading strategies can be analyzed precisely using a reinforcement learning technique. The added benefit of using this technique is its ability to study an optimal trading strategy and to increase the value of the portfolio using one programming instruction.  

4. Inventory management:- Coordination of inventory policies adopted by manufacturers, suppliers, and distributors for smooth flowing of materials while reducing the cost is the major issue faced by the inventory management. RL algorithms can be developed to minimize transmitting time stocking, retrieving the products and optimizing warehouse operations. 

Due to the continuous advancements, technology giants are articulating the significant long-term RL strategies and their outcomes. However, due to its limitations like large time-consumptions to perform activities when the action space is large, using RL can be quite challenging and would require further study to resolve the hardships. 

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