Tag Archives: Reinforcement Learning
Reinforcement Learning: Machine Learning Meets Control Theory
Tips for Running High-Fidelity Deep Reinforcement Learning Experiments
Despite recent incredible algorithmic advances in the field, deep reinforcement learning (DRL) remains notorious for being computationally expensive, prone to “silent bugs”, and difficult to tune hyperparameters. These phenomena make running high-fidelity, scientifically-rigorous reinforcement learning experiments paramount.
In this article, I will discuss a few tips and lessons I’ve learned to mitigate the effects of these difficulties in DRL — tips I never would have learned from a reinforcement learning class. Read More
Reinforcement Learning At Facebook with Jason Gauci
If you ever wanted to learn about machine learning you could do worse than have Jason Gauci teach you. Jason has worked on YouTube recommendations. He was an early contributor to TensorFlow the open-source machine learning platform. His thesis work was cited by DeepMind. Read More
Discrete Latent Space World Models for Reinforcement Learning
Sample efficiency remains a fundamental issue of reinforcement learning. Model-based algorithms try to make better use of data by simulating the environment with a model. We propose a new neural network architecture for world models based on a vector quantized-variational autoencoder (VQ-VAE) to encode observations and a convolutional LSTM to predict the next embedding indices. A model-free PPO agent is trained purely on simulated experience from the world model. We adopt the setup introduced by Kaiser et al. (2020), which only allows100Kinteractionswith the real environment, and show that we reach better performance than their SimPLe algorithm in five out of six randomly selected Atari environments, while our model is significantly smaller. Read More
Deep reinforcement-learning architecture combines pre-learned skills to create new sets of skills on the fly
A team of researchers from the University of Edinburgh and Zhejiang University has developed a way to combine deep neural networks (DNNs) to create a new type of system with a new kind of learning ability. The group describes their new architecture and its performance in the journal Science Robotics.
Deep neural networks are able to learn functions by training on multiple examples repeatedly. To date, they have been used in a wide variety of applications such as recognizing faces in a crowd or deciding whether a loan applicant is credit-worthy. In this new effort, the researchers have combined several DNNs developed for different applications to create a new system with the benefits of all of its constituent DNNs. Read More
Alphabet’s Loon hands the reins of its internet air balloons to self-learning AI
Alphabet’s Loon, the team responsible for beaming internet down to Earth from stratospheric helium balloons, has achieved a new milestone: its navigation system is no longer run by human-designed software.
Instead, the company’s internet balloons are steered around the globe by an artificial intelligence — in particular, a set of algorithms both written and executed by a deep reinforcement learning-based flight control system that is more efficient and adept than the older, human-made one. The system is now managing Loon’s fleet of balloons over Kenya, where Loon launched its first commercial internet service in July after testing its fleet in a series of disaster relief initiatives and other test environments for much of the last decade. Read More
ReBeL: A general game-playing AI bot that excels at poker and more
Combining reinforcement learning with search (RL+Search) has been tremendously successful for perfect-information games. But prior RL+Search algorithms break down in imperfect-information games. We introduce ReBeL, an algorithm that for the first time enables sound RL+Search in imperfect-information games like poker.
ReBeL achieves superhuman performance in heads-up no-limit Texas Hold’em while using far less domain knowledge than any prior poker bot and extends to other imperfect-information games as well, such as Liar’s Dice, for which we’ve open-sourced our implementation.
ReBeL is a major step toward creating ever more general AI algorithms. Read More
Creating Next-Gen Video Game AI With Reinforcement Learning
Learn how reinforcement learning is being used to upend traditional methods of creating video game AI
Reinforcement learning stands to become the new gold standard in creating intelligent video game AI. The chief advantage of reinforcement learning(RL) over traditional game AI methods is that, rather than hand-crafting the AI’s logic using complicated behavior trees, with RL one simply rewards the behavior they wish the AI to manifest and the agent learns by itself to perform the necessary sequence of actions to achieve the desired behavior. In essence, this is how one might teach a dog to perform tricks using a food reward.
The RL approach to game AI can be used to train a variety of strategic behaviors, including path finding, NPC attack and defense, and almost every behavior a human is capable of exhibiting while playing a video game. Read More
Deep reinforcement learning, symbolic learning and the road to AGI
Tim Rocktäschel on the TDS podcast
This episode is part of our podcast series on emerging problems in data science and machine learning, hosted by Jeremie Harris. Apart from hosting the podcast, Jeremie helps run a data science mentorship startup called SharpestMinds. Read More
Rick's Cafe AI 