154 citations · 448 across the 14 of their papers we have counts for
16 papers
Towards practical reinforcement learning for tokamak magnetic control
Brendan D. Tracey, Andrea Michi, Yuri Chervonyi +15
Reinforcement learning (RL) has shown promising results for real-time control systems, including the domain of plasma magnetic control. However, there are still significant drawbac…
From Motor Control to Team Play in Simulated Humanoid Football
Siqi Liu, Guy Lever, Zhe Wang +19
Intelligent behaviour in the physical world exhibits structure at multiple spatial and temporal scales. Although movements are ultimately executed at the level of instantaneous mus…
Real World Games Look Like Spinning Tops
Wojciech Marian Czarnecki, Gauthier Gidel, Brendan Tracey +4
This paper investigates the geometrical properties of real world games (e.g. Tic-Tac-Toe, Go, StarCraft II). We hypothesise that their geometrical structure resemble a spinning top…
Caveats for information bottleneck in deterministic scenarios
Artemy Kolchinsky, Brendan D. Tracey, Steven Van Kuyk
Information bottleneck (IB) is a method for extracting information from one random variable that is relevant for predicting another random variable . To do so, IB identifies…
Upgrading from Gaussian Processes to Student's-T Processes
Brendan D. Tracey, David H. Wolpert
Gaussian process priors are commonly used in aerospace design for performing Bayesian optimization. Nonetheless, Gaussian processes suffer two significant drawbacks: outliers are a…
Deep Reinforcement Learning for Event-Driven Multi-Agent Decision Processes
Kunal Menda, Yi-Chun Chen, Justin Grana +4
The incorporation of macro-actions (temporally extended actions) into multi-agent decision problems has the potential to address the curse of dimensionality associated with such de…
Estimating Mixture Entropy with Pairwise Distances
Artemy Kolchinsky, Brendan D. Tracey
Mixture distributions arise in many parametric and non-parametric settings -- for example, in Gaussian mixture models and in non-parametric estimation. It is often necessary to com…
Nonlinear Information Bottleneck
Artemy Kolchinsky, Brendan D. Tracey, David H. Wolpert
Information bottleneck (IB) is a technique for extracting information in one random variable that is relevant for predicting another random variable . IB works by encoding $…
Modeling Social Organizations as Communication Networks
David Wolpert, Justin Grana, Brendan Tracey +2
We identify the "organization" of a human social group as the communication network(s) within that group. We then introduce three theoretical approaches to analyzing what determine…
Reducing the error of Monte Carlo Algorithms by Learning Control Variates
Brendan D. Tracey, David H. Wolpert
Monte Carlo (MC) sampling algorithms are an extremely widely-used technique to estimate expectations of functions f(x), especially in high dimensions. Control variates are a very p…
Dynamics of beneficial epidemics
Andrew Berdahl, Christa Brelsford, Caterina De Bacco +12
Pathogens can spread epidemically through populations. Beneficial contagions, such as viruses that enhance host survival or technological innovations that improve quality of life,…
Predicting the behavior of interacting humans by fusing data from multiple sources
Erik J. Schlicht, Ritchie Lee, David H. Wolpert +2
Multi-fidelity methods combine inexpensive low-fidelity simulations with costly but highfidelity simulations to produce an accurate model of a system of interest at minimal cost. T…
Cyber-Physical Security: A Game Theory Model of Humans Interacting over Control Systems
Scott Backhaus, Russell Bent, James Bono +5
Recent years have seen increased interest in the design and deployment of smart grid devices and control algorithms. Each of these smart communicating devices represents a potentia…
Counter-Factual Reinforcement Learning: How to Model Decision-Makers That Anticipate The Future
Ritchie Lee, David H. Wolpert, James Bono +3
This paper introduces a novel framework for modeling interacting humans in a multi-stage game. This "iterated semi network-form game" framework has the following desirable characte…
Predicting the behavior of interacting humans by fusing data from multiple sources
Erik J. Schlicht, Ritchie Lee, David H. Wolpert +2
Multi-fidelity methods combine inexpensive low-fidelity simulations with costly but high-fidelity simulations to produce an accurate model of a system of interest at minimal cost.…
Using Supervised Learning to Improve Monte Carlo Integral Estimation
Brendan Tracey, David Wolpert, Juan J. Alonso
Monte Carlo (MC) techniques are often used to estimate integrals of a multivariate function using randomly generated samples of the function. In light of the increasing interest in…