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Reinforcement Learning


Simplifying Reinforcement Learning Workflow in MATLAB

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Imagine you were interested in solving a certain problem using Reinforcement learning. You have coded in your environment and you compile a laundry list of Reinforcement Learning (RL) algorithms to try. Self-implementing the algorithms from scratch is tricky and time-consuming because it requires a lot of trials and contains a lot of implementational tricks. The best answer is to use an RL framework. RL framework contains near-optimal implementations of RL algorithms.


Advanced AI: Deep Reinforcement Learning in Python

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This course is all about the application of deep learning and neural networks to reinforcement learning. If you've taken my first reinforcement learning class, then you know that reinforcement learning is on the bleeding edge of what we can do with AI. Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level. Reinforcement learning has been around since the 70s but none of this has been possible until now. The world is changing at a very fast pace.


Entropy in Soft Actor-Critic (Part 1)

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In the probability theory, there are two principles associated with entropy: the principle of maximum entropy and the principle of minimum cross-entropy. At very beginning we notice that there are two types of entropy, however there are more in stock. First of all, let us emphasize that neither the principle of maximum entropy nor the principle of minimum cross-entropy are theorems, they are only principles of statistical inference. This is very similar to philosophical doctrine. However, these doctrines certainly have mathematical implications. So we have two different types of entropy: entropy and cross-entropy.


This new robotics challenge could bring us closer to human-level AI

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This makes one appreciate the complexity of human vision and agency. The next time you go to a supermarket, consider how easily you can find your way through aisles, tell the difference between different products, reach for and pick up different items, place them in your basket or cart, and choose your path in an efficient way. And you're doing all this without access to segmentation and depth maps and by reading items from a crumpled handwritten note in your pocket. The TDW-Transport Challenge is in the process of accepting submissions. In the meantime, the authors of the paper have already tested the environment with several known reinforcement learning techniques. Their findings show that pure reinforcement learning is very poor at solving task and motion planning challenges.


Reinforcement learning competition pushes the boundaries of embodied AI

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This highlights the complexity of human vision and agency. The next time you go to a supermarket, consider how easily you can find your way through aisles, tell the difference between different products, reach for and pick up different items, place them in your basket or cart, and choose your path in an efficient way. And you're doing all this without access to segmentation and depth maps and by reading items from a crumpled handwritten note in your pocket. Above: Experiments show hybrid AI models that combine reinforcement learning with symbolic planners are better suited to solving the ThreeDWorld Transport Challenge. The TDW-Transport Challenge is in the process of accepting submissions.


The Sequence Scope: The Race to Improve Reinforcement Learning

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The Sequence Scope is a summary of the most important published research papers, released technology and startup news in the AI ecosystem in the last week. This compendium is part of TheSequence newsletter. Data scientists, scholars, and developers from Microsoft Research, Intel Corporation, Linux Foundation AI, Google, Lockheed Martin, Cardiff University, Mellon College of Science, Warsaw University of Technology, Universitat Politècnica de València and other companies and universities are already subscribed to TheSequence. Reinforcement Learning (RL) has been at the center of some of the most important milestones of the last decade of deep learning. DeepMind's RL-based AlphaGo is considered by many the "Sputnik moment" in artificial intelligence (AI), responsible for sparking an innovation race between the top AI labs in the world.


The Ubiquity and Future of Model-based Reinforcement Learning

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As many of you know, I am doing my PhD centered around model-based reinforcement learning (MBRL). This post is not talking about the technical details and recent work, but rather why I am bullish on it for the future. Beyond the prospects of how well it can perform (it's much younger than most of deep RL), having discussions with AI Safety and Ethical AI experts makes it clear that it's more structured learning-setup is pointing towards systems that humans can better understand. Some level of understanding how the system makes decisions is likely a prerequisite for many companies to start using it, else they cannot do real A/B testing and analysis. I will start by showing you the rich set of parallels MBRL has in biological processes, and then show the features making it more suitable for safe deployment in society-facing systems (see why this matters here).


Reinforcement learning challenge to push boundaries of embodied AI

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This makes one appreciate the complexity of human vision and agency. The next time you go to a supermarket, consider how easily you can find your way through aisles, tell the difference between different products, reach for and pick up different items, place them in your basket or cart, and choose your path in an efficient way. And you're doing all this without access to segmentation and depth maps and by reading items from a crumpled handwritten note in your pocket. The TDW-Transport Challenge is in the process of accepting submissions. In the meantime, the authors of the paper have already tested the environment with several known reinforcement learning techniques.


Transforming animation with machine learning

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Animation is a big bottleneck in all game development. Characters or creatures have to be designed and scripted manually, to achieve seemingly realistic interactions with the world. That makes it hard to achieve scale without growing your game team. So over the past two years, we've continued down the path of physical animation based on reinforcement learning. In short, that means we train physically-based machines to walk by giving them rewards for doing the right things -- like virtual dog treats.


Financial Engineering and Artificial Intelligence in Python

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Created by Lazy Programmer Team, Lazy Programmer Inc.Preview this Course - GET COUPON CODE Have you ever thought about what would happen if you combined the power of machine learning and artificial intelligence with financial engineering? Today, you can stop imagining, and start doing. This course will teach you the core fundamentals of financial engineering, with a machine learning twist. We will cover must-know topics in financial engineering, such as: Exploratory data analysis, significance testing, correlations, alpha and beta Time series analysis, simple moving average, exponentially-weighted moving average Holt-Winters exponential smoothing model Efficient Market Hypothesis Random Walk Hypothesis Time series forecasting ("stock price prediction") Modern portfolio theory Efficient frontier / Markowitz bullet Mean-variance optimization Maximizing the Sharpe ratio Convex optimization with Linear Programming and Quadratic Programming Capital Asset Pricing Model (CAPM) Algorithmic trading (VIP only) Statistical Factor Models (VIP only) Regime Detection with Hidden Markov Models (VIP only) In addition, we will look at various non-traditional techniques which stem purely from the field of machine learning and artificial intelligence, such as: Classification models Unsupervised learning Reinforcement learning and Q-learning ***VIP-only sections (get it while it lasts!) You will learn exactly why their methodology is fundamentally flawed and why their results are complete nonsense.