Magnetic control of tokamak plasmas through deep reinforcement learning
Nuclear fusion using magnetic confinement, in particular in the tokamak configuration, is a
promising path towards sustainable energy. A core challenge is to shape and maintain a …
promising path towards sustainable energy. A core challenge is to shape and maintain a …
Outracing champion Gran Turismo drivers with deep reinforcement learning
Many potential applications of artificial intelligence involve making real-time decisions in
physical systems while interacting with humans. Automobile racing represents an extreme …
physical systems while interacting with humans. Automobile racing represents an extreme …
Acme: A research framework for distributed reinforcement learning
Deep reinforcement learning (RL) has led to many recent and groundbreaking advances.
However, these advances have often come at the cost of both increased scale in the …
However, these advances have often come at the cost of both increased scale in the …
Beyond supervised continual learning: a review
Continual Learning (CL, sometimes also termed incremental learning) is a flavor of machine
learning where the usual assumption of stationary data distribution is relaxed or omitted …
learning where the usual assumption of stationary data distribution is relaxed or omitted …
Open source vizier: Distributed infrastructure and api for reliable and flexible blackbox optimization
Vizier is the de-facto blackbox optimization service across Google, having optimized some of
Google's largest products and research efforts. To operate at the scale of tuning thousands …
Google's largest products and research efforts. To operate at the scale of tuning thousands …
Whole-body simulation of realistic fruit fly locomotion with deep reinforcement learning
The body of an animal influences how the nervous system produces behavior. Therefore,
detailed modeling of the neural control of sensorimotor behavior requires a detailed model …
detailed modeling of the neural control of sensorimotor behavior requires a detailed model …
Active offline policy selection
This paper addresses the problem of policy selection in domains with abundant logged data,
but with a restricted interaction budget. Solving this problem would enable safe evaluation …
but with a restricted interaction budget. Solving this problem would enable safe evaluation …
Phantom--A RL-driven multi-agent framework to model complex systems
Agent based modelling (ABM) is a computational approach to modelling complex systems
by specifying the behaviour of autonomous decision-making components or agents in the …
by specifying the behaviour of autonomous decision-making components or agents in the …
Increasing the safety of adaptive cruise control using physics-guided reinforcement learning
This paper presents a novel approach for improving the safety of vehicles equipped with
Adaptive Cruise Control (ACC) by making use of Machine Learning (ML) and physical …
Adaptive Cruise Control (ACC) by making use of Machine Learning (ML) and physical …
GEAR: a GPU-centric experience replay system for large reinforcement learning models
This paper introduces a distributed, GPU-centric experience replay system, GEAR, designed
to perform scalable reinforcement learning (RL) with large sequence models (such as …
to perform scalable reinforcement learning (RL) with large sequence models (such as …