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10:09

DeepMind Makes Prototyping Papers Easy with ACME

DeepMind’s ACME framework makes implementing deep reinforcement learning agents incredibly easy. By using a modularized approach to agent design, agents can be scaled from a single thread up to hundreds easily. In this video I’ll give you a brief overview of how all the pieces fit together. Learn how to turn deep reinforcement learning papers […]
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Getting Started with VIM as a Python Editor

Learn how to turn deep reinforcement learning papers into code: Get instant access to all my courses, including the new Hindsight Experience Replay course, with my subscription service. $24.99 a month gives you instant access to 24 hours of instructional content plus access to future updates, added monthly. Discounts available for Udemy students (enrolled longer […]
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05:23

Have high gpu prices got you down?

GTC 2022 is almost upon us. That means it’s time for a giveaway. All you have to do is subscribe to the channel, watch the keynote, take a screenshot of yourself in the keynote session and email it to phil@neuralnet.ai. The keynote speech is March 22 at 8 AM PST. GPU giveaway is restricted to […]
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How I learned to stop worrying and love Artificial Super Intelligence

I think fears of artiicial super intelligence (in pop culture, specifically) are a bit overblown. I lay out my case in this vodeo. Learn how to turn deep reinforcement learning papers into code: Get instant access to all my courses, including the new Hindsight Experience Replay course, with my subscription service. $24.99 a month gives […]
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09:13

Reinforcement learning overview (Reinforcement learning with TensorFlow Agents)

Wei Wei, a Developer Advocate for TensorFlow, kicks off a new series on reinforcement learning where we explore how you can leverage TensorFlow Agents to build your own reinforcement learning agents. Wei explains how reinforcement learning can be used to train agents to make the best decisions when performing actions in environments to maximize rewards. […]
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01:22:15

Trends in Deep Reinforcement Learning with Kamyar Azizzadenesheli – #560

Today we’re joined by Kamyar Azizzadenesheli, an assistant professor at Purdue University, to close out our AI Rewind 2021 series! In this conversation, we focused on all things deep reinforcement learning, starting with a general overview of the direction of the field, and though it might seem to be slowing, thats just a product of […]
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19:29

Mastering Robotics with Hindsight Experience Replay | Paper Analysis

Hindisght experience replay works pretty simply: swap out the original goal your agent was trying to receive with one it actually received. It deals with environments with sparse rewards and large state spaces. Check out my analysis of the paper here. Learn how to turn deep reinforcement learning papers into code: Get instant access to […]
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10:28

Why I'm Not Putting My New Course On Udemy

Learn how to turn deep reinforcement learning papers into code: Get instant access to all my courses, including the new Hindsight Experience Replay course, with my subscription service. $24.99 a month gives you instant access to 24 hours of instructional content plus access to future updates, added monthly. Discounts available for Udemy students (enrolled longer […]
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09:48

Introduction to TF Agents and Deep Q Learning (Reinforcement learning with TensorFlow Agents)

Wei Wei, a Developer Advocate for TensorFlow, introduces TF Agents and walks through how to use the Deep Q Learning model to solve the cartpole environment. Resources: TensorFlow Agents homepage → https://goo.gle/34i7MAI Train a Deep Q Network with TF Agents Tutorial → https://goo.gle/3oz26ZQ TF-Agent DQN example → https://goo.gle/3HxmXnM Reinforcement Learning Lecture Series 2021 (DeepMind x […]
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Proximal Policy Optimization is Easy with Tensorflow 2 | PPO Tutorial

Proximal Policy Optimization (PPO) has emerged as a powerful on policy actor critic algorithm. You might think that implementing it is difficult, but in fact tensorflow 2 makes coding up a PPO agent relatively simple. We’re going to take advantage of my PyTorch code for this, as it serves as a great basis to expand […]
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14:54

Basic Hyperparameter Tuning in DeepMinds ACME Framework

In today’s ACME deep reinforcement learning framework tutorial, I will showy ou how to do some basic hyperparameter tuning in their built in Deep Q Learning agent. Learn how to turn deep reinforcement learning papers into code: Deep Q Learning: https://www.udemy.com/course/deep-q-learning-from-paper-to-code/?couponCode=DQN-AUG-2021 Actor Critic Methods: https://www.udemy.com/course/actor-critic-methods-from-paper-to-code-with-pytorch/?couponCode=AC-AUG-2021 Natural Language Processing from First Principles: https://www.udemy.com/course/natural-language-processing-from-first-principles/?couponCode=NLP1-AUG-2021 Reinforcement Learning Fundamentals […]
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25:46

Getting Started with Encryption in 2022

When you think of encryption you probably think of highly comjplex algorithms like SHA-256, but you can actually get unbreakable encryption with only a few lines of python. We’ll see how in this tutorial. We are going to cover 3 of the fundamental algorithms in encryption: the Caesar cipher, the Vignere cipher, and the one […]
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How to Code RL Agents Like DeepMind

DeepMind is known for leading the way in deep reinforcement learning research. Creating novel agents to conquer the most advanced environments requires the use of some sophisticated infrastructure. Fortunately for us mere mortals, they’ve open sourced their framework for designing deep reinforcement learning agents: ACME. In ACME, you’ll find everything from deep Q learning all […]
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Two Minute Papers: Digital Creatures Learn to Navigate in 3D | Two Minute Papers #153

The paper “DeepLoco: Dynamic Locomotion Skills Using Hierarchical Deep Reinforcement Learning” is available here: http://www.cs.ubc.ca/~van/papers/2017-TOG-deepLoco/index.html Two Minute Papers Merch: US: http://twominutepapers.com/ EU/Worldwide: https://shop.spreadshirt.net/TwoMinutePapers/ WE WOULD LIKE TO THANK OUR GENEROUS PATREON SUPPORTERS WHO MAKE TWO MINUTE PAPERS POSSIBLE: Andrew Melnychuk, Christian Lawson, Dave Rushton-Smith, Dennis Abts, e, Esa Turkulainen, Michael Albrecht, Sunil Kim, VR Wizard. […]
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Two Minute Papers: Deep Learning From Human Preferences | Two Minute Papers #196

The paper “Deep Reinforcement Learning from Human Preferences” is available here: https://arxiv.org/pdf/1706.03741.pdf Our Patreon page with the details: https://www.patreon.com/TwoMinutePapers We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Andrew Melnychuk, Brian Gilman, Dave Rushton-Smith, Dennis Abts, Eric Haddad, Esa Turkulainen, Evan Breznyik, Kaben Gabriel Nanlohy, Malek Cellier, Michael Albrecht, […]
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03:00

Two Minute Papers: Deep Reinforcement Terrain Learning | Two Minute Papers #67

In this piece of work, a combination of deep learning and reinforcement learning is presented which has proven to be useful in solving many extremely difficult tasks. Google DeepMind built a system that can play Atari games at a superhuman level using this technique that is also referred to as Deep Q-Learning. This time, it […]
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02:31

Two Minute Papers: This Robot Learned To Clean Up Clutter

The paper “Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning” is available here: http://vpg.cs.princeton.edu/ Pick up cool perks on our Patreon page: › https://www.patreon.com/TwoMinutePapers We would like to thank our generous Patreon supporters who make Two Minute Papers possible: 313V, Andrew Melnychuk, Angelos Evripiotis, Anthony Vdovitchenko, Brian Gilman, Christian Ahlin, Christoph Jadanowski, […]
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04:31

CoRL 2020, Spotlight Talk 250: Flightmare: A Flexible Quadrotor Simulator

**Flightmare: A Flexible Quadrotor Simulator** Yunlong Song (ETH / University of Zurich)*; Selim Naji (ETH / Univ. of Zurich); Elia Kaufmann (ETH / University of Zurich); Antonio Loquercio (ETH / University of Zurich); Davide Scaramuzza (University of Zurich & ETH Zurich, Switzerland) Publication: http://corlconf.github.io/paper_250/ **Abstract** State-of-the-art quadrotor simulators have a rigid and highly-specialized structure: either […]
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CoRL 2020, Spotlight Talk 450: Deep Reinforcement Learning with Population-Coded Spiking Neural N…

“**Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control** Guangzhi Tang (Rutgers University); Neelesh Kumar (Rutgers University); Raymond Yoo (Rutgers University); Konstantinos Michmizos (Rutgers University)* Publication: http://corlconf.github.io/paper_450/ **Abstract** The energy-efficient control of mobile robots has become crucial as the complexity of their real-world applications increasingly involves high-dimensional observation and action spaces, which cannot […]
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CoRL 2020, Spotlight Talk 516: Multi-Level Structure vs. End-to-End-Learning in High-Performance …

“**Multi-Level Structure vs. End-to-End-Learning in High-Performance Tactile Robotic Manipulation** Florian Voigt (Technical University of Munich)*; Lars Johannsmeier (Technical University of Munich); Sami Haddadin (Technical University of Munich) Publication: http://corlconf.github.io/paper_516/ **Abstract** In this paper we apply a multi-level structure to robotic manipulation learning. It consists of a hybrid dynamical system we denote skill and a parameter […]
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CoRL 2020, Spotlight Talk 245: Learning to Walk in the Real World with Minimal Human Effort

**Learning to Walk in the Real World with Minimal Human Effort** Sehoon Ha (Georgia Institute of Technology); Peng Xu (Google Inc); Zhenyu Tan (Google); Sergey Levine (UC Berkeley)*; Jie Tan (Google) Publication: http://corlconf.github.io/paper_245/ **Abstract** Reliable and stable locomotion has been one of the most fundamental challenges for legged robots. Deep reinforcement learning (deep RL) has […]
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