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The proliferation of reinforcement learning

The previous year has been chock full of massive advantages in the fields of machine learning and artificial intelligence. GPT-3 can now reliably generate writing that tricks many individuals into thinking that it was authored by a human being. DeepMind’s AlphaFold can now determine with precision, a protein’s 3D shape with its nucleotide sequence, a major step forward for the medical field. The hazards to individual privacy presented by ever-powerful face recognition continues to experience ongoing growth. Customer smartphones and notebooks now integrate specialist hardware to accelerate machine learning workloads. 

Generally, both AI scientists and real-world enterprises are leveraging machine learning and artificial intelligence technologies into new spheres that were prior assumed to be the domain of human beings alone. A few subfields, such as language models and self-driving vehicles, are gradually enhancing, demonstrating huge annual improvements regardless of a lack of overt breakthroughs.  

Among these huge advancements in artificial intelligence, one advancement is an outlier: the appreciating prevalence of reinforcement learning. (RL) This technology, while not brand new, is becoming more widespread, particularly in spheres where other variants of machine learning stumble to produce results. By the end of this blog post, you’ll be aware what reinforcement learning is, how it functions, and how it can provide solutions to real-world issues in a broad array of industries. 

Reinforcement Learning 101 

Machine learning strategies can be categorized into one of three primary objectives: 

  • Supervised learning, where an ML model is trained by providing it with a problem with the right solution. With the passage of time, the model generates outputs which are closer to the right solution, not just with regards to inputs it has observed prior, but additionally on novel, innovative questions, demonstrating that this learning is doing more than just mere memorization. To put it in different words, the dataset given to a supervised learning framework is labelled.  
  • Unsupervised learning, in which the data set is not labelled. The model identifies novel patterns that may have been prior not known to human beings, but it doesn’t learn from the “right” answer given as part of the dataset. 
  • Reinforcement learning, where the framework, also referred to as an agent, obtains feedback from its surroundings. Just like with supervised learning, a model leveraging reinforcement learning persistently enhances and learns from its errors. But, unlike supervised learning, an exterior supervisor or dataset developer does not give the right output for every input, enabling the model to possibly discover prior unseen answers. 

Reinforcement learning is referred to as such due to the fact that its behaviours are reinforced again and again via +ve and -ve feedback. If a reinforcement model is attempting to learn to manipulate a video game, it will learn to persist leveraging strategies that function well and stop doing stuff that doesn’t turn out well. 

In contrast to supervised and unsupervised learning, reinforcement learning is relatively nascent. Richard S. Sutton is hailed as one of the founding fathers of the modern approaches towards reinforcement learning, with his 1984 Ph.D. thesis putting forth concepts that are still in widespread use today. 

Instances of Reinforcement Learning 

One of the major use cases for reinforcement learning is stuff that move about in the physical world. For instance, autonomous vehicles and industrial bot devices usually leverage reinforcement learning. Vacuum bots such as Roomba are a brilliant, easy to access instance of reinforcement learning in the physical world, they identify hurdles and go about planning routes to clean your home in a swift fashion without bumping into anything. 

Here are a few critical qualities that make an issue possibly an apt fit for reinforcement learning. 

  • Possess an easy to quantify reward or fitness function that informs the model how well its output conforms to the surroundings. For instance, in maze-solving reinforcement learning models, a +ve result from a fitness function is shifting closer to the end. To put it in different words, issues apt for reinforcement learning have their basis in rewards. 
  • Make predictions on an ongoing basis and evaluate them in the surroundings. If a model must make all of its forecasts at the same time, it will not have the time to go about learning from the outcomes when enhancing subsequent performance. 
  • Accept errors. As reinforcement learning consists of trial and error, issues that need precision from the start should leverage supervised learning – or something other than ML instead. 

Every variant of machine learning has its own specific niches. There are definitely some scenarios that are better resolved with supervised or unsupervised learning over reinforcement learning. For instance, issues that need enhanced accuracy and have a large pre-solved dataset available function better with supervised learning, while the ones that need pattern matching perform well with unsupervised learning. 

Reinforcement learning’s niche lies in spheres where its tradeoffs are palatable and more unique outcomes are needed. As an instance, reinforcement learning succeeds when you can simply tell how well an issue has been resolved but is unable to furnish a big number of existing issues and optimal answers. 

Tech organizations such as Amazon, Google, and Facebook develop recommendation engines into many aspects of their products. These engines furnish personalized suggest to clients on the basis of their historical preferences. Leveraging reinforcement learning, recommendation engines can furnish gradually improved recommendations on the basis of how clients react to the suggestions. Additional, reinforcement learning can learn even as an individual’s likes and dislikes alter over time – a problem that other variants of solutions have trouble with. 

In spheres such as autonomous vehicles, smart vacuums, and robotic process automation, reinforcement learning provides new possibilities that previously couldn’t be acomplished with other variants of machine learning. Not one of these instances could be simply solved with datasets, with or without labelling. As an outcome, reinforcement learning is the ideal answer. 

Applications for Reinforcement Learning 

Due to its capability to identify solutions to issues without a preliminary dataset or any exterior supervision, reinforcement learning is good for a broad variety of scenarios where other variants of machine learning are not practical. While several industry applications with regards to reinforcement learning are still being produced, there are some obvious cases where the technology demonstrates undeniable potentials. 

RL in Healthcare: Drug Discovery and Healthcare 

Several of the most critical and significant medicines leveraged in medicine today were identified later on by trial and error or arbitrary chance. Popularly, penicillin was discovered from mould present on spoilt food and much later made into a form apt for medicine. What if software could increase the efficiency of that trial and error procedure? 

In a rather unfortunate turn of events, conventional reinforcement learning puts forth a plethora of issues when applied to medicine discovery. Most critically, it would be very unethical and far too slow to utilize live patients to go about training the reinforcement learning model. Instead, scientists have resorted to the analysis and forecasting of the impact of medicines in historical data from prior studies. 

In several ways, reinforcement learning emulates evolution. The reward or fitness function occurring naturally is an organism’s survival mechanism, while it can be manually modified in reinforcement learning models. As an outcome, reinforcement learning is apt to the simulation of natural processes that can be good in medicine.  

RL in Quantitative finance: Automated Stock Trading 

Hedge funds and quantitative finance firms already leverage sophisticated artificial intelligence when making automated trades on stock exchanges, often finished in the fraction of a second. While the stock market is a lot more tough to forecast than most issues that reinforcement learning is applied to, this variant of machine learning can still be a good part of a broader trading strategy. 

As one instance, RBC’s Aiden is a now public, very sophisticated stock trading utility that leverages reinforcement learning-based deep learning. The utility is efficient enough that RBC goes about investing a portion of their client’s money leveraging the technology. 

By persisting in learning from its errors and adapting to broader market situations, reinforcement learning in quantitative finance puts forth growth avenues that were not present with other variants of models. Specifically, reinforcement learning needs reduced manual modifications in contrast to current models that do not leverage machine learning. Finance firms do not have to invest as much time resources developing and updating models to be apt for market scenarios. 

RL in video gaming: More intelligent AI 

NPCs and adversaries in video gaming are typical use cases for artificial intelligence. A few of these characters have funnily simplistic AI, often standing out like a sore thumb when pegged against photorealistic game graphics. Older and more typical strategies, usually fast variations of pathfinding algorithms, leave a lot of room for improvement in terms of authenticity.  

In addition to AI in a game, RL can also be leveraged to play games more efficiently. ML has for long had application in games such as Chess and Go, however, RL provide great avenues in far more complicated and sophisticated games. Open-ended games like Minecraft and even FPS can be brilliant use cases for RL-driven automation. These AI players may even identify effective strategies that humans couldn’t identify. 


Reinforcement learning is one of three dominating branches of machine learning. Unlike unsupervised and supervised learning, RL does not need any dataset. As an outcome, RL is the ideal answer for a broad variety of issues that can be solved via trial and error. 

With such great prospects in spheres like self-driving vehicles, high-frequency asset trading and even video gaming, reinforcement learning is certain to be a dominant part of the artificial intelligence situation in 2021 and beyond. 

  1. Having said that, a few might opine that RL is a mature technology that is under-leveraged in industry. This is partially due to the fact that 
  2. Not an adequate number of individuals are aware about reinforcement learning, and hence, are not aware how to ID good problems and 
  3. There are so many useful uses of supervised learning, people might concentrate on low hanging fruit and what they are aware of, over thinking outside the box. 
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