Currently, a research assistant at IIIT-Delhi working on representation learning in Deep RL. Ex - Mathworks, DRDO.
This post covers how to implement a custom environment in OpenAI Gym. As an example, we implement a custom environment that involves flying a Chopper (or a helicopter) while avoiding obstacles mid-air.
In this post, we describe the anatomy of how most Deep Reinforcement Learning algorithms work. We also cover the motivation to use RL over standard machine learning, On-Policy v/s Off-Policy learning, the Exploration-Exploitation Tradeoff, and many more important RL concepts.
In this article, we'll cover the basic building blocks of Open AI Gym. This includes environments, spaces, wrappers, and vectorized environments.
We cover basic mistakes that can lead to unnecessary copying of data and memory allocation in NumPy. We further cover NumPy internals, strides, reshaping, and transpose in detail.
In this part we'll see how to speed up an implementation of the k-means clustering algorithm by 70x using NumPy. We cover how to use cProfile to find bottlenecks in the code, and how to address them using vectorization.
In Part 1 of our series on writing efficient code with NumPy we cover why loops are slow in Python, and how to replace them with vectorized code. We also dig deep into how broadcasting works, along with a few practical examples.
Using PyTorch, we create a COVID-19 classifier that predicts whether a patient is suffering from coronavirus or not, using chest CT scans of different patients.
