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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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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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26:44

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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40:38

SingularityNET: Modeling COVID-19 Using Simulated Agents with Intelligence and Culture – Dr. Deborah Duong

➡️ COVID-19 Simulation Summit Playlist: https://www.youtube.com/playlist?list=PLAJnaovHtaFR5puHCN4W_4o8cgIHdawDb ——————————————— 👀 About the speaker Dr. Deborah Duong is Director or AI Development at Rejuve and Director of Network Analytics at Singularity Net. The focus of her research is Complex Adaptive Systems, on the boundary between AI and Computational Social Science. She wrote the world’s first Intelligent Agent Based […]
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04:53

CoRL 2020, Spotlight Talk 442: Hierarchical Robot Navigation in Novel Environments using Rough 2-…

“**Hierarchical Robot Navigation in Novel Environments using Rough 2-D Maps** Chengguang Xu (Northeastern University)*; Christopher Amato (Northeastern University); Lawson Wong (Northeastern University) Publication: http://corlconf.github.io/paper_442/ **Abstract** In robot navigation, generalizing quickly to unseen environments is essential. Hierarchical methods inspired by human navigation have been proposed, typically consisting of a high-level landmark proposer and a low-level controller. […]
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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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04:58

CoRL 2020, Spotlight Talk 476: Explicitly Encouraging Low Fractional Dimensional Trajectories Via…

“**Explicitly Encouraging Low Fractional Dimensional Trajectories Via Reinforcement Learning** Sean Gillen (UCSB)*; Katie Byl (UCSB) Publication: http://corlconf.github.io/paper_476/ **Abstract** A key limitation in using various modern methods of machine learning in developing feedback control policies is the lack of appropriate methodologies to analyze their long-term dynamics, in terms of making any sort of guarantees (even statistically) […]
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04:04

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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03:31

Two Minute Papers: Reinforcement Learning with OpenAI’s Gym | Two Minute Papers #72

OpenAI’s Gym is available here: https://gym.openai.com/ OpenAI – Non-profit AI company by Elon Musk and Sam Altman Google DeepMind’s paper “Unifying Count-Based Exploration and Intrinsic Motivation” and video on reniforcement learning and curiosity: https://arxiv.org/pdf/1606.01868v1.pdf Link to the mentioned research project at Experiment: 1. https://experiment.com/projects/opening-your-mind-s-eye-collaborating-with-a-computer-to-reveal-visual-imagination?s=discover 2. https://experiment.com/projects/yvgjmnuxsnavvjuhxzwf WE WOULD LIKE TO THANK OUR GENEROUS PATREON SUPPORTERS […]
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03:41

CoRL 2020, Spotlight Talk 30: Learning a Decentralized Multi-Arm Motion Planner

“**Learning a Decentralized Multi-Arm Motion Planner** Huy Ha (Columbia University); Jingxi Xu (Columbia University); Shuran Song (Columbia University)* Publication: http://corlconf.github.io/paper_30/ **Abstract** We present a closed-loop multi-arm motion planner that is scalable and flexible with team size. Traditional multi-arm robotic systems have relied on centralized motion planners, whose run times often scale exponentially with team size, […]
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02:20

Two Minute Papers: Artificial Neural Networks and Deep Learning | Two Minute Papers #3

Artificial neural networks provide us incredibly powerful tools in machine learning that are useful for a variety of tasks ranging from image classification to voice translation. So what is all the deep learning rage about? The media seems to be all over the newest neural network research of the DeepMind company that was recently acquired […]
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05:05

CoRL 2020, Spotlight Talk 381: Learning Object-conditioned Exploration using Distributed Soft Act…

“**Learning Object-conditioned Exploration using Distributed Soft Actor Critic** Ayzaan Wahid (Google)*; Austin C Stone (Google); Kevin Chen (Stanford); Brian Ichter (Google Brain); Alexander Toshev (Google) Publication: http://corlconf.github.io/paper_381/ **Abstract** Object navigation is defined as navigating to an object of a given label in a complex, unexplored environment. In its general form, this problem poses several challenges […]
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04:59

CoRL 2020, Spotlight Talk 104: PixL2R: Guiding Reinforcement Learning Using Natural Language by M…

“**PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards** Prasoon Goyal (The University of Texas at Austin)*; Scott Niekum (UT Austin); Raymond Mooney (Univ. of Texas at Austin) Publication: http://corlconf.github.io/paper_104/ **Abstract** Reinforcement learning (RL), particularly in sparse reward settings, often requires prohibitively large numbers of interactions with the environment, thereby limiting its […]
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Two Minute Papers: Reinforcement Learning With Noise (OpenAI) | Two Minute Papers #225

The paper “Better Exploration with Parameter Noise” and its source code is available here: https://arxiv.org/abs/1706.01905 https://github.com/openai/baselines The write-up and our Patreon page with the details: https://www.patreon.com/posts/technical-for-16738692 https://www.patreon.com/TwoMinutePapers We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Andrew Melnychuk, Brian Gilman, Christian Ahlin, Christoph Jadanowski, Dave Rushton-Smith, Dennis Abts, Emmanuel, […]
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04:27

CoRL 2020, Spotlight Talk 496: Harnessing Distribution Ratio Estimators for Learning Agents with …

“**Harnessing Distribution Ratio Estimators for Learning Agents with Quality and Diversity** Tanmay Gangwani (University of Illinois, Urbana Champaign)*; Jian Peng (University of Illinois at Urbana-Champaign); Yuan Zhou (UIUC) Publication: http://corlconf.github.io/paper_496/ **Abstract** Quality-Diversity (QD) is a concept from Neuroevolution with some intriguing applications to Reinforcement Learning. It facilitates learning a population of agents where each member […]