9 citations · 19 across the 6 of their papers we have counts for
6 papers
STARC: A General Framework For Quantifying Differences Between Reward Functions
Joar Skalse, Lucy Farnik, Sumeet Ramesh Motwani +3
In order to solve a task using reinforcement learning, it is necessary to first formalise the goal of that task as a reward function. However, for many real-world tasks, it is very…
imitation: Clean Imitation Learning Implementations
Adam Gleave, Mohammad Taufeeque, Juan Rocamonde +7
imitation provides open-source implementations of imitation and reward learning algorithms in PyTorch. We include three inverse reinforcement learning (IRL) algorithms, three imita…
Calculus on MDPs: Potential Shaping as a Gradient
Erik Jenner, Herke van Hoof, Adam Gleave
In reinforcement learning, different reward functions can be equivalent in terms of the optimal policies they induce. A particularly well-known and important example is potential s…
Preprocessing Reward Functions for Interpretability
Erik Jenner, Adam Gleave
In many real-world applications, the reward function is too complex to be manually specified. In such cases, reward functions must instead be learned from human feedback. Since the…
Extensions of Karger's Algorithm: Why They Fail in Theory and How They Are Useful in Practice
Erik Jenner, Enrique Fita Sanmartín, Fred A. Hamprecht
The minimum graph cut and minimum --cut problems are important primitives in the modeling of combinatorial problems in computer science, including in computer vision and mach…
Steerable Partial Differential Operators for Equivariant Neural Networks
Erik Jenner, Maurice Weiler
Recent work in equivariant deep learning bears strong similarities to physics. Fields over a base space are fundamental entities in both subjects, as are equivariant maps between t…