My paper: A Comprehensive Review on Deep Reinforcement Learning
I recently wrote a survey paper on "A Comprehensive Review on Deep Reinforcement Learning: A Survey", with some of the leading AI and DRL researchers (including): In this work, we covered top recent DRL works, grouped into several categories. We were lucky to have you, as the external reviewers of this work. I hope this is useful for the research community. Any feedback will be highly welcomed. You can find its summary here too. Imitation learning, expert (teacher), hierarchical, hybrid imitation, high performance parallelism,
Simple Deep Q Network w/Pytorch: https://youtu.be/UlJzzLYgYoE
Reinforcement Learning Crash Course: https://youtu.be/sOiNMW8k4T0
Policy Gradients w/Tensorflow: https://youtu.be/UT9pQjVhcaU
Deep Q Learning w/Tensorflow https://youtu.be/3Ggq_zoRGP4
Code Your Own RL Environments https://youtu.be/vmrqpHldAQ0
How to Spec a Deep Learning PC: https://youtu.be/xsnVlMWQj8o
Deep Q Learning w/ Pytorch: https://youtu.be/RfNxXlO6BiA
Machine Learning Freelancing https://youtu.be/6M04ZTLE_O4
Code from video: https://github.com/philtabor/Youtube-Code-Repository
Notes and info
training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world
Lately, simulation has helped achieve impressive results in reinforcement learning, which is extremely data-intensive.
using reinforcement learning to train robots that reason about how their actions will affect their environment.
How is it that many people learn to drive a car fairly safely in 20 hours of practice, while current imitation learning algorithms take hundreds of thousands of hours, and reinforcement learning algorithms take millions of hours? Clearly we’re missing something big.
In 2021, I expect self-supervised methods to learn features of video and images. Could there be a similar revolution in high-dimensional continuous data like video?
One critical challenge is dealing with uncertainty. Models like BERT can’t tell if a missing word in a sentence is “cat” or “dog,” but they can produce a probability distribution vector. We don’t have a good model of probability distributions for images or video frames. But recent research is coming so close that we’re likely to find it soon.
Suddenly we’ll get really good performance predicting actions in videos with very few training samples, where it wasn’t possible before. That would make the coming year a very exciting time in AI.
DeepMind released the code, model, & dataset behind their groundbreaking "AlphaFold" system. It's predicts protein shapes from genomic data with apps in health, sustainability, & materials design
DeepMind Open-Sources Lab2D, A System For The Creation Of 2D Environments For Machine Learning