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Top 5 Prerequisites For Mastering The Deep Reinforcement Learning Skills
By Kamal Jacob
The world is changing rapidly as the technological wave is sweeping across the globe. Most of the companies today are embracing Artificial Technology to automate their systems, create efficiency and improve performance levels as it provides them with immense computing power to make sense of Big Data at affordable rates and without any hassles. According to a research report, Artificial Intelligence will be contributing $15.7 trillion to the world economy by the year 2030.
Machine Learning is just one subset of Artificial Intelligence. Check out how endless possibilities can be achieved with Artificial Intelligence & Machine Learning here.
Deep Learning Now Evolved into Deep Reinforcement Learning…What’s the Difference?
Bundled under Machine Learning is Deep Learning which, in simple terms, can be described as making machines smart like humans in a way that they start thinking intelligently, learn and act based on past data and experiences which could be either someone else’s or even their own.
Understanding Deep Learning behaviors in machines can be as simple as equating it with Deep Learning behaviors in human beings. For example, when you first learn something as complex as how to ride a bicycle or drive a car to as simple as even cutting vegetables, you are moving all over the place and making mistakes, but with practice over time by learning from your previous experiences, you hone and perfect your skills to do that activity correctly. In the process, these humans/algorithms are penalized when they make the wrong decisions and rewarded when they make the right ones – that’s what reinforcement of good behaviors is all about through the autonomous modification of behaviors/algorithms.
Learn how Deep Learning can be applied in a real-life environment and situations to maximize returns here.
Deep Learning has further evolved into Reinforcement Learning wherein Reinforcement Learning diverges from both Unsupervised Learning and Supervised Learning (other subsets of Machine Learning) in the way that it interprets the inputs. Let’s take an example to illustrate the differences.
There is one ‘thing’ kept on the table. This serves as the ‘input’ for the various learning algorithms that are applied (mentioned above). The interpretations would be:
Reinforcement Learning encompasses goal-oriented algorithms which learn how to maximize returns/rewards along a particular dimension over multiple steps or attain a complex goal or objective. It is a self-teaching and independent system which learns on-the-go by doing using the trial and error method and tries to arrive at the best possible outcomes from a random number of possible actions. Reinforcement Learning can be seen as a kind of Supervised Learning in an environment which provides sparse feedback when interacted with.
In fact, the difference between Deep Learning and Deep Reinforcement Learning is very small that too only in the way the learning takes place. Deep Learning involves learning from the existing data and then applying that learning/knowledge to a new data set. On the other hand, Reinforcement Learning involves learning dynamically using the trial and error method to make informed decisions by adjusting the actions based on the feedback that is being continuously received from the environment in the process of attempting those actions.
In this way, Deep Learning and Reinforcement Learning can’t be seen as ‘mutually exclusive’. In fact, it is as simple as understanding the below formulae:
Deep Reinforcement Learning = Deep Learning Algorithms * Reinforcement Learning System
Top 5 Prerequisites to Master Deep Reinforcement Learning Skills
Due to the increased interest of enterprises in Deep Reinforcement Learning and its innovative capabilities, it has become the hottest job of the season and will continue to remain the top choice for professionals looking out for jobs in the tech industry. However, they would need to equip themselves with certain specific skills and knowledge to master the art effectively.
Here are the top 5 prerequisites to master Deep Reinforcement Learning:
1. Neural Networks - Neural networks are the agents that learn to map state-action pairs to rewards. They do so by finding and using the right coefficients/weights to approximate the function connecting inputs to outputs by iteratively adjusting those coefficients/weights along the gradients that promise less or no error.
Deep Learning algorithms help develop artificial neural networks which imitate the neuron networks in the human brain. For example, it could be used to distinguish between females and males in images by classifying and clustering the image data such as distances between the shapes and other specifics in the existing photos to predict the identification in a new set of images.
We have curated a list of the best resources to get you started with:
§ Blogs/Websites –
2. Edwin Chen
5. Cortana Intelligence and Machine Learning Blog
6. Open AI
7. Fast.ai
§ Influencers to follow -
3. Yann LeCun
§ Tools –
1. Keras
2. TensorFlow
3. PyTorch
4. ai.google
5. Blocks
6. ConvNet
7. Neon
§ Events/conferences/hackathons –
1. Kaggle
§ Courses –
1. https://www.manipalprolearn.com/data-science/neural-networks-tensorflow
2. https://www.manipalprolearn.com/data-science/deep-learning-tensorflow-0
2. Python – It is a general-purpose, interactive, high-level, object-oriented scripting/programming language designed to be highly readable. It is the major code language for Machine Learning & Artificial Intelligence for reasons that it has a low barrier, a great library ecosystem within itself, platform independence, provides flexibility and has a range of good visualization options to choose from. Hence, there is no doubt that Python is the most popular choice among data scientists.
Want to learn Python and how it can be used in data science? Check it out here.
We have curated a list of the best resources to get you started with:
§ Blogs/Websites –
1. Codecademy
2. DataCamp
§ Influencers to follow –
1. Guido van Rossum (creator of python language)
§ Courses –
https://www.manipalprolearn.com/data-science/programming-data-science-using-python
3. Probability – Whenever data is utilized in a system without a sole logic, the level of uncertainty grows and probability becomes even more relevant as it is the science of quantifying uncertain things. Common sense is introduced into the Deep Learning system by applying probability theorems. The concepts of Conditional Probability, Bell-Curve Model of Normal Distribution and Bayes’ Theorem are more prominent in Deep Reinforcement Learning as compared to other probability models used.
Read more about their applicability in Deep Reinforcement Learning here.
We have curated a list of the best resources to get you started with:
§ Blogs/Websites –
1. Stat 110 (Introduction to Probability) by Joe Blitzstein
2. Stat 110 (Introduction to Probability) videos and tutorials available on YouTube
§ Influencers to follow –
4. Dynamic Programming – It is a method used for solving complex problems by breaking them down into sub-problems, finding solutions for these sub-problems and then combining them again to solve the overall problem. The two prerequisites for using Dynamic Programming are:
Overlapping subproblems: The solutions of the sub-problems that can be cached and reused as they recur many times in the entire process.
Optimal substructure: The optimal solutions of the sub-problems that can be used to solve the overall problem.
It is worthwhile to mention the use of mathematical frameworks here like Markov Decision Processes (MDPs) that solve Reinforcement Learning problems by satisfying the properties of the Bellman Equation and the Value function. Read the beginner’s guide to Deep Reinforcement Learning to clarify concepts here.
We have curated a list of the best resources to get you started with:
§ Blogs/Websites/Books –
1. Introduction to Algorithms by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein (CLRS)
3. LeetCode
4. HackerEarth
§ Influencers to follow –
5. Linear Algebra – It is a branch of mathematics that lets you describe the coordinates and the interactions of planes in higher dimensions concisely, and thereby perform operations on them. It is useful in Machine Learning in the sense that one can describe complex operations using the formalisms, notations and matrix factorization from linear algebra.
Using Linear Algebra in Deep Reinforcement Learning can serve as building blocks for deeper intuition. You can even get more out of the existing algorithms, implement algorithms from scratch, devise new algorithms, the possibilities are endless.
We have curated a list of the best resources to get you started with:
§ Blogs/Websites/Books –
1. Introduction to Linear Algebra by Gilbert Strang
2. Linear Algebra: A Modern Introduction by David Poole
3. NPTEL
§ Influencers to follow –
2. David Poole
There are many open-source, special-purpose simulation toolkits and environments that working profesionals can use to train, practice and up their game in Deep Reinforcement Learning. Some of them are listed below:
1. Psychlab
2. House3D
3. DeepMind Lab
4. PyBrain
5. Teachingbox
Conclusion
The artificial intelligence industry is growing at such an exponential rate that we can now confidently say that ‘Machine and man together can be better than the man himself’. Although Deep Learning, Machine Learning and Reinforcement Learning are highly interconnected to each other, none of them specifically can replace the others, but each of them can build upon one another and evolve further into newer and more advanced technologies.
And that’s a wrap! Hope this blog post proves to be insightful for you! Let me know your thoughts in the comments section below! Learning Machine Learning can be quite a task if you do not have the appropriate resources and mentorship with you. Manipal ProLearn’s courses on Deep Learning, Machine Learning and Data Science prepare you for today’s industry. Check them out here!
Kamal Jacob
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