AI Technology & Industry Review
A research team from UC Berkeley and Carnegie Mellon University proposes a task-agnostic reinforcement learning method that reduces the task-specific engineering required for domain randomization of both visual and dynamics parameters.
A research team from DeepMind introduces Anakin and Sebulba, two architectures that demonstrate reinforcement learning platforms based on TPUs can efficiently deliver exceptional performance at scale and with low cost.
Stanford researchers’ DERL (Deep Evolutionary Reinforcement Learning) is a novel computational framework that enables AI agents to evolve morphologies and learn challenging locomotion and manipulation tasks in complex environments using only low level egocentric sensory information.
A research team from DeepMind and University College London have released Alchemy, a novel open-source benchmark for meta-RL research.
Researchers from Vector Institute, University of Toronto and Google Brain recently studied three common types of intrinsic motivation across seven agents, three Atari games, and the 3D game Minecraft.
This year, 22 Transformer-related research papers were accepted by NeurIPS, the world’s most prestigious machine learning conference. Synced has selected ten of these works to showcase the latest Transformer trends.
A new DeepMind scalable environment simulator takes a digital approach to the question, enabling the examination of environmental factors on AI agents.
A new study proposes using human feedback and interaction logs to boost offline reinforcement learning (RL) in natural language processing (NLP).
DeepMind and Universidade de Lisboa introduce graph-based toolkit for analyzing and comparing multiplayer games
In a new paper, researchers from Google, OpenAI, and DeepMind introduce “behaviour priors,” a framework designed to capture common movement and interaction patterns that are shared across a set of related tasks or contexts.
Japanese AI startup Preferred Networks (PFN) is moving ChainerRL to the PyTorch ecosystem.
Augmented Temporal Contrast (ATC), a new unsupervised learning (UL) task for learning visual representations agnostic to rewards and without degrading the control policy.
The AI Economist uses a two-level RL framework where a collection of AI agents simulate how humans might react to various taxes in a principled economic simulation.
Researchers from Stanford University, UC Berkeley and CMU review neuromechanical simulations and DRL, with a focus on modelling the control of human locomotion.
DeepMind and McGill University researchers believe the problem could be solved through a strategic “divide-and-conquer” approach.
Researchers conducted a thorough empirical study of hyperparameter selection for offline RL, aiming to identify and develop more reliable and effective approaches.
PFRL succeeds ChainerRL as comprehensive library with cutting-edge deep reinforcement learning algorithms and features.
Researchers proposed a Taylor Expansion Policy Optimization (TayPO) framework that combines two leading algorithmic improvement methods.
DeepMind researchers call attention to another “fundamentally novel” development in AI research — deep reinforcement learning (deep RL) — which they believe also has vital implications for neuroscience and deserves more attention from neuroscientists.
NLE is a procedurally generated environment for testing the robustness and systematic generalization of RL agents.
DeepMind researchers released several new models and a tutorial for their dm_control software stack for physics-based simulation and reinforcement learning environments using MuJoCo physics.
Researchers from the University of Tokyo and Google Research have proposed a new metric for RL performance and novel BREMEN algorithm designed to manage the costs and risks of new policy deployment.
DeepMind researchers introduce a framework that aims to solve the problem by enabling simple RL agent implementations to be run at different scales of execution.
Enter Plan2Explore — a self-supervised RL agent designed to quickly generalize to unseen tasks in a zero or few-shot manner.
The proposed concurrent DRL algorithm enables robots to take a broader and more long-term view of tasks and behaviours, and decide on their next action before the current one is completed.
Agent57 is the first deep reinforcement learning (RL) agent to top human baseline scores on all games in the Atari57 test set.
Researchers from Google Brain Tokyo and Google Japan have proposed a novel approach that helps guide reinforcement learning (RL) agents to what’s important in vision-based tasks.
DeepMind announced yesterday the release of Haiku and RLax — new JAX libraries designed for neural networks and reinforcement learning respectively.
In an effort to sustain RL’s momentum, a team of researchers from Machine Zone, Google Brain, and California Institute of Technology have introduced a new software framework and benchmark for reproducible reinforcement learning research.
Using cross-modality transfer techniques and reinforcement learning (RL), the researchers produced an agent that can play video games with only the game audio to guide it.
Precup discussed the latest developments in the field: “Reinforcement learning allows autonomous agents to learn how to act in a stochastic, unknown environment, with which they can interact.”
A team of researchers from Texas A&M University and Canada’s Simon Fraser University have open-sourced a toolkit called “RLCard” for applying RL research to card games.
Synced Global AI Weekly October 20th
At last week’s Re•Work AI in Finance Conference in New York, researchers and engineers from banks and academia alike shared their thoughts on current AI research and applications in the finance world.
A new paper from San Francisco-based OpenAI proposes training models in the children’s game of hide-and-seek and pitting them against each other.
Now AI researchers have created a similar evaluation system to assess the relative performance of reinforcement learning (RL) systems across different tasks.
In common reinforcement learning (RL) domains, existing keypoint discovery methods struggle to perceive keypoints in an image when there are limited objects present.
The traditional retail industry is undergoing a significant reinvention and upgrade as more and more brick and mortar stores boost business by adopting e-commerce platforms powered by cutting-edge tech.
Berkeley Artificial Intelligence Research (BAIR) has introduced a new reinforcement learning (RL) method, Stochastic Optimal Control with Latent Representations (SOLAR), which can help robots quickly learn tasks such as stacking blocks or pushing objects from visual inputs.
A group of AI experts from top US universities is organizing a sample-efficient reinforcement learning competition, MineRL, which will start on June 1, 2019. The organizers want to increase group participation in reinforcement learning and are encouraging people to “play to benefit science”.