In this post, I am going to give a summary of nine key papers from the distributional reinforcement learning (DRL) area. Paper 001 : A Distributional Perspective on Reinforcement Learning This is the seminal paper in this area. The key idea of the paper is the argument that the value distribution is important in reinforcement
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I always aspired to be a great thinker. My quest for this skill led me to various types of thinking from different sources. The ones I have encountered so far are summarized below. I am jotting down my thoughts on each of these areas. Critical Thinking This is the top in the list and is
This article is a tutorial style discussion of the research paper titled “Distributional RL with Quantile Regression”. Here I try to reflect and convey the ideas of the paper with an intuitive style. Introduction Supervised machine learning deals with learning from examples. This is in contrast to real-life where we often don’t have a proper
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A few years ago, this post (Confession as AI Researcher) on machine learning Reddit community got a lot of attention. It was asking for machine learning research advice. A lot of suggestions/advice have been made by the community. I am summarizing all the points made by different community members along with my thoughts. Hope this
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This is a curated list of resources, blogs, YouTube channels/playlists, and special interest groups which discuss machine learning papers. These machine learning reading groups are an excellent opportunity for anyone working in machine learning to improve their knowledge and skillset and also to network remotely. For any machine learning or data science aspirant or even
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I always asked myself what is the best way to do various things. I mean to do more in less time and in an efficient manner. Things like how to study better?. How to think better?. How to manage time better?. How to solve problems quickly?. How to wake up early?. Such questions are always
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Introduction This post is going to be a short and birds-eye view of probability theory. During my undergraduate days, I often wonder what is the use of probability theory provided its fluctuating nature. For example chance of getting ahead by tossing a fair coin is 0.5. Suppose we got the head the first time, there
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Bellman operators come in Reinforcement Learning (RL). When I first encountered it, I had many questions regarding it. I often feel it is interesting to observe that what feels intriguing to someone. Many questions surface out in our minds. Why is it called an operator?. What are the inputs to it and output from it?.
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In this post, we will see how to animate matplotlib graphs. In many situations you might wish to create an animated content of some dynamic graph or process. You might want to do this to explain or understand a concept better. It is especially appealing for people like me who cannot think in abstract terms.
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Overview In this note about distributional reinforcement learning, I am going to reflect on the paper titled A Distributional Perspective on Reinforcement Learning. I will try to give an overview of the underlying ideas behind this paper. Please keep in mind that a research contribution is often a culmination of multiple ideas/components. This makes from
Summary of research paper “A Distributional Perspective on Reinforcement Learning” Read More »