AI Technology & Industry Review
In a new paper Faster sorting algorithms discovered using deep reinforcement learning, a DeepMind research team introduces AlphaDev, a deep reinforcement learning agent which is capable to automatically discover correct and efficient sorting algorithms that achieves superior performance then previously known human benchmarks.
In the new paper FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing, a UC Berkeley research team proposes FastRLAP (Fast Reinforcement Learning via Autonomous Practicing), a system that autonomously practices in the real world and learns aggressive maneuvers to enable effective high-speed driving.
In the new paper Learning Robust Real-Time Cultural Transmission Without Human Data, a DeepMind research team proposes a procedure for training artificially intelligent agents capable of flexible, high-recall, robust real-time cultural transmission from human co-players in a rich 3D physical simulation without using human data in the training pipeline.
A research team from ETH Zurich and NVIDIA proposes a training framework that enables fast policy generation for real-world robotic tasks — training time can be reduced by multiple orders of magnitude using massive parallelism on a single workstation GPU.
A Nvidia research team presents Isaac Gym — a high-performance robotics simulation platform that runs an end-to-end GPU accelerated training pipeline. Compared to conventional RL training methods that use a CPU-based simulator and GPU for neural networks, Isaac Gym achieves training speedups of 2-3 orders of magnitude on continuous control tasks.
A research team from ETH and UC Berkeley proposes a Deep Reward Learning by Simulating the Past (Deep RLSP) algorithm that represents rewards directly as a linear combination of features learned through self-supervised representation learning and enables agents to simulate human actions backwards in time to infer what they must have done.
MIT, UChicago, Harvard and Diffeo *Bayesian Delegation* multi-agent learning mechanism enables agents to coordinate behaviour on the fly
University of Zurich and ETH Zurich’s deep reinforcement learning agents have achieved superhuman performance in the time trials of popular racing simulator game Gran Turismo Sport.
In a new paper, researchers from the New York University and Modl.ai, a company applying machine learning to game developing, suggest that simple spacial processing methods such as rotation, translation and cropping could help increase model generality.
The Autonomous Learning Library is a deep reinforcement learning (DRL) library for PyTorch that streamlines the building and evaluation of novel reinforcement learning agents.
In a recent paper Tencent AI Lab researchers present a deep reinforcement learning (DRL) approach to handle the complex action control of agents in MOBA 1v1 games.
There are three ways to combine DL and RL, based on three different principles: value-based, policy-based, and model-based approaches with planning.