(testing signal)

Tag: reinforcementlearning

A Basic Overview of the Reinforcement Learning Techniques Behind DeepMind’s AlphaStar

AlphaStar has evolved in two versions achieving superhuman performance in StarCraft IISource: https://venturebeat.com/2019/10/30/deepminds-alphastar-final-beats-99-8-of-human-starcraft-2-players/I recently started an AI-focused educational newsletter, that already has over 100,000 subscribers. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and…… Read more...

A journey towards faster Reinforcement Learning

From Icarus burning his wings to the Wright brothers soaring through the sky, it took mankind thousands of years to learn how to fly, but how long will it take an AI to do the same?

In this article, we will be reviewing a practical aspect of Reinforcement Learning (RL): how to make it faster! My journey into Reinforcement Learning has been a wonderful experience, going from theoretical knowledge to applied experiments. However, one thing that really grinds my gears is having to wait for the agent to finish training before trying up another idea to improve my project. So, one day I decided to find ways to make the whole process faster.… Read more...

Using PettingZoo with RLlib for Multi-Agent Deep Reinforcement Learning

A tutorial on using PettingZoo multi-agent environments with the RLlib reinforcement learning library

Continue reading: https://towardsdatascience.com/using-pettingzoo-with-rllib-for-multi-agent-deep-reinforcement-learning-5ff47c677abd?source=rss—-7f60cf5620c9—4

Source: towardsdatascience.com
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👑 Big Tech and their Favorite Deep Learning Schools

📝 Editorial Last week we started a new series focused on self-supervised learning (SSL). You will notice that many relevant papers and tech come from Facebook AI Research (FAIR). This is because FAIR has become the leading AI lab championing SSL, but this is not an isolated pattern. Most of the top AI labs in the world have sort of picked different schools of deep learning to champion. Here are some of my favorite examples:  DeepMind — Reinforcement Learning: Without a doubt,…… Read more...

Are Tech Firms Investing More In Reinforcement Learning Research? – Analytics India Magazine

Reinforcement learning (RL) is one of the most exciting prospects that a data scientist may add to their resume today. Many IT companies, such as Google, Amazon, Microsoft, IBM, Sony, and others, have established research centres and AI labs in India throughout the years. RL has been at the heart of some of the most significant breakthroughs in recent years. Let’s take a look at some of the prominent companies that are active in RL.

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Amazon’s Recommendation Systems 

According to research, systems that apply reinforcement learning can change recommendations based on user feedback continuously.

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Stay updated with Neuroscience: August 2021 Must-Reads

How infants learn speaking 🗣️?; Harvard 🏛️ defines a new maths framework for biologically 🧠 inspired Reinforcement Learning; Neuronal avalanches modelling 🏂

Why should you care about Neuroscience?

Neuroscience is the root of nowadays artificial intelligence 🧠🤖. Reading and being aware of the evolution and new insights in neuroscience not only will allow you to be a better “Artificial Intelligence” guy 😎, but also a finer neural network architectures creator 👩‍💻!

August proposes three complicated papers, that I tried to make simple and digestible. I guess many of you have always wondered how infants can put all the sounds together and learn languages 🗣️ Rohrlich and O’Reilly from the University of California Davis try to assess this question, developing fully biologically inspired neural networks, which proves experimental findings on infants — already tested in 1996!

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An Introduction to Reinforcement Learning with OpenAI Gym, RLlib, and Google Colab

An introductory tutorial on reinforcement learning with OpenAI Gym, RLlib, and Google Colab

This tutorial will use reinforcement learning (RL) to help balance a virtual CartPole. The video above from PilcoLearner shows the results of using RL in a real-life CartPole environment.
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DeepMind’s Collect & Infer: A Fresh Look at Data-Efficient Reinforcement Learning | Synced

In recent years there has been growing interest in reinforcement learning (RL) algorithms that can learn entirely from fixed datasets without interaction (offline RL). A number of relatively unexplored challenges remain in this research field, such as how to get the most out of the collected data, how to work with growing datasets, and how to compose the most effective datasets.

In a new paper, a DeepMind research team proposes a clear conceptual separation of the RL process into data-collection and inference of knowledge to improve RL data efficiency. The team introduces a “Collect and Infer” (C&I) paradigm and provides insights on how to interpret RL algorithms from the C&I perspective; while also showing how it could guide future research into more data-efficient RL.

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Introduction to Direct Reinforcement Learning by Example

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How to Use Reinforcement Learning to Recommend Content

A new RL framework develop by researchers at Google.

Figure 1: the reinforcement learning framework. Image by author.
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Can Deep Reinforcement Learning Solve Chess?

  • Introduction to DRL
  • Implementing DRL
  • Analyzing Results
  • Conclusion

Note: All of the code is in the form of snippets and will not work when executed alone. The full code can be found on my Github repo.

Reinforcement learning is the training of an agent to make decisions in an environment. An agent is deployed in an environment. At any given frame, the agent must use data from the environment to act. This action will yield a reward value, which represents the quality of the action. The agent will then accordingly update its process to maximize this reward.

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Three Baseline Policies Your Reinforcement Learning Algorithm Absolutely Should Outperform

Being a subdomain of Machine Learning, Reinforcement Learning (RL) is often likened to a black box. You try a couple of actions, feed the resulting observations into a neural network, and out roll some values — an esoteric policy telling you what to do in any given circumstance.

When traversing a frozen lake or playing a videogame, you will see soon enough whether that policy is of any use. However, there are many problems out there without a clear notion of solution quality, without lower and upper bounds, without visual aids. Think of controlling a large fleet of trucks, of rebalancing a stock portfolio over time, of determining order policies for a supermarket.

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How to Design a Reinforcement Learning Reward Function for a Lunar Lander

Reward Function in Reinforcement Learning

The rules in reward function of lunar lander

How to represent the rules in python code

Code by Author
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Ultimate Volleyball: A 3D physics-based RL environment built using Unity ML-Agents

Train reinforcement learning agents to play Volleyball

Image by Author
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