16 citations · 35 across the 6 of their papers we have counts for
6 papers
Fast transport simulations with higher-fidelity surrogate models for ITER
J. Citrin, P. Trochim, T. Goerler +3
A fast and accurate turbulence transport model based on quasilinear gyrokinetics is developed. The model consists of a set of neural networks trained on a bespoke quasilinear GENE…
Semi-analytical Industrial Cooling System Model for Reinforcement Learning
Yuri Chervonyi, Praneet Dutta, Piotr Trochim +9
We present a hybrid industrial cooling system model that embeds analytical solutions within a multi-physics simulation. This model is designed for reinforcement learning (RL) appli…
Evaluating model-based planning and planner amortization for continuous control
Arunkumar Byravan, Leonard Hasenclever, Piotr Trochim +8
There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches. In this paper we attempt to evaluate this i…
Learning Dynamics Models for Model Predictive Agents
Michael Lutter, Leonard Hasenclever, Arunkumar Byravan +5
Model-Based Reinforcement Learning involves learning a \textit{dynamics model} from data, and then using this model to optimise behaviour, most often with an online \textit{planner…
Using Unity to Help Solve Intelligence
Tom Ward, Andrew Bolt, Nik Hemmings +10
In the pursuit of artificial general intelligence, our most significant measurement of progress is an agent's ability to achieve goals in a wide range of environments. Existing pla…
Augmenting learning using symmetry in a biologically-inspired domain
Shruti Mishra, Abbas Abdolmaleki, Arthur Guez +2
Invariances to translation, rotation and other spatial transformations are a hallmark of the laws of motion, and have widespread use in the natural sciences to reduce the dimension…