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29:08

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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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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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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05:21

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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05:13

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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05:03

CoRL 2020, Spotlight Talk 203: Contrastive Variational Reinforcement Learning for Complex Observa…

**Contrastive Variational Reinforcement Learning for Complex Observations** Xiao Ma (National University of Singapore)*; SIWEI CHEN (National University of Singapore); David Hsu (NUS); Wee Sun Lee (National University of Singapore) Publication: http://corlconf.github.io/paper_203/ **Abstract** Deep reinforcement learning (DRL) has achieved significant success in various robot tasks: manipulation, navigation, etc. However, complex visual observations in natural environments remains […]
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04:51

CoRL 2020, Spotlight Talk 256: Hardware as Policy: Mechanical and Computational Co-Optimization u…

“**Hardware as Policy: Mechanical and Computational Co-Optimization using Deep Reinforcement Learning** Tianjian Chen (Columbia University)*; Zhanpeng He (Columbia University); Matei Ciocarlie (Columbia) Publication: http://corlconf.github.io/paper_256/ **Abstract** Deep Reinforcement Learning (RL) has shown great success in learning complex control policies for a variety of applications in robotics. However, in most such cases, the hardware of the robot […]
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CoRL 2020, Spotlight Talk 130: Motion Planner Augmented Reinforcement Learning for Robot Manipula…

“**Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments** Jun Yamada (University of Southern California); Youngwoon Lee (University of Southern California)*; Gautam Salhotra (University of Southern California); Karl Pertsch (University of Southern California); Max Pflueger (University of Southern California); Gaurav Sukhatme (University of Southern California); Joseph Lim (USC); Peter Englert (University of Southern […]
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05:05

CoRL 2020, Spotlight Talk 439: Deep Reactive Planning in Dynamic Environments

“**Deep Reactive Planning in Dynamic Environments** Kei Ota (Mitsubishi Electric Corporation)*; Devesh Jha (MERL); Tadashi Onishi (Mitsubishi Electric); Asako Kanezaki (Tokyo Institute of Technology); Yusuke Yoshiyasu (AIST); Yoko Sasaki (National Institute of Advanced Industrial Science and Technology); Toshisada Mariyama (Mitsubishi Electric); Daniel Nikovski () Publication: http://corlconf.github.io/paper_439/ **Abstract** The main novelty of the proposed approach is […]
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09:33

Chip floorplanning with deep reinforcement learning

Learn about a deep reinforcement learning method that can generate superhuman chip layouts in under six hours, rather than weeks or months of human effort. This method was recently published in Nature and was used in production to generate chip layouts for Google’s latest AI accelerator (TPU). Speakers: Anna Goldie (Staff Research Scientist), Azalia Mirhoseini […]
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CoRL 2020, Spotlight Talk 91: SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Ob…

“**SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation** Xingyu Lin (Carnegie Mellon University)*; Yufei Wang (Carnegie Mellon University); Jake Olkin (CMU); David Held (CMU) Publication: http://corlconf.github.io/paper_91/ **Abstract** Manipulating deformable objects has long been a challenge in robotics due to its high dimensional state representation and complex dynamics. Recent success in deep reinforcement learning provides […]
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08:24

Announcing the RTX 3090 Winner

The time has come to announce the winner of the giveaway. I’m giving away 5 courses, a swag bag, and an RTX 3090. The GPU and swag are only for US based viewers, due to the current situation with supply chains / all the issues in the world. Congratulations to all our winners! Learn how […]
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01:35:43

Machine Learning with Phil Live Stream

GTC 2021 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 https://www.manning.com/livevideo/reinforcement-learning-in-motion Here are some books / courses I recommend (affiliate links): Grokking Deep Learning in Motion: https://bit.ly/3fXHy8W Grokking Deep Learning: https://bit.ly/3yJ14gT Grokking Deep Reinforcement Learning: […]
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DeepMind’s AI Learns Imagination-Based Planning | Two Minute Papers #178

The paper “Imagination-Augmented Agents for Deep Reinforcement Learning” is available here: https://arxiv.org/abs/1707.06203 Out 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, Christian Lawson, Dave Rushton-Smith, Dennis Abts, e, Eric Swenson, Esa Turkulainen, Kaben Gabriel Nanlohy, Michael Albrecht, Michael Orenstein, Steef, […]
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OpenAI Safety Gym: A Safe Place For AIs To Learn 💪

❤️ Check out Linode here and get $20 free credit on your account: https://www.linode.com/papers 📝 The paper “Benchmarking Safe Exploration in Deep Reinforcement Learning” is available here: https://openai.com/blog/safety-gym/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Alex Haro, Anastasia Marchenkova, Andrew Melnychuk, Angelos Evripiotis, Anthony Vdovitchenko, Benji […]
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