Brax--a differentiable physics engine for large scale rigid body simulation
We present Brax, an open source library for rigid body simulation with a focus on
performance and parallelism on accelerators, written in JAX. We present results on a suite of …
performance and parallelism on accelerators, written in JAX. We present results on a suite of …
Gradients are not all you need
Differentiable programming techniques are widely used in the community and are
responsible for the machine learning renaissance of the past several decades. While these …
responsible for the machine learning renaissance of the past several decades. While these …
Physics-informed machine learning for modeling and control of dynamical systems
Physics-informed machine learning (PIML) is a set of methods and tools that systematically
integrate machine learning (ML) algorithms with physical constraints and abstract …
integrate machine learning (ML) algorithms with physical constraints and abstract …
Optimal rates for bandit nonstochastic control
Abstract Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) control
are foundational and extensively researched problems in optimal control. We investigate …
are foundational and extensively researched problems in optimal control. We investigate …
Online nonstochastic model-free reinforcement learning
We investigate robust model-free reinforcement learning algorithms designed for
environments that may be dynamic or even adversarial. Traditional state-based policies …
environments that may be dynamic or even adversarial. Traditional state-based policies …
Training efficient controllers via analytic policy gradient
Control design for robotic systems is complex and often requires solving an optimization to
follow a trajectory accurately. Online optimization approaches like Model Predictive Control …
follow a trajectory accurately. Online optimization approaches like Model Predictive Control …
Controlgym: Large-scale safety-critical control environments for benchmarking reinforcement learning algorithms
We introduce controlgym, a library of thirty-six safety-critical industrial control settings, and
ten infinite-dimensional partial differential equation (PDE)-based control problems …
ten infinite-dimensional partial differential equation (PDE)-based control problems …
Controlgym: Large-scale control environments for benchmarking reinforcement learning algorithms
We introduce controlgym, a library of thirty-six industrial control settings, and ten infinite-
dimensional partial differential equation (PDE)-based control problems. Integrated within the …
dimensional partial differential equation (PDE)-based control problems. Integrated within the …
Online Learning for Obstacle Avoidance
We approach the fundamental problem of obstacle avoidance for robotic systems via the
lens of online learning. In contrast to prior work that either assumes worst-case realizations …
lens of online learning. In contrast to prior work that either assumes worst-case realizations …
A regret minimization approach to multi-agent control
We study the problem of multi-agent control of a dynamical system with known dynamics
and adversarial disturbances. Our study focuses on optimal control without centralized …
and adversarial disturbances. Our study focuses on optimal control without centralized …