2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 1 cited
On The Fragility of Learned Reward Functions
Lev McKinney, Yawen Duan, David Krueger +1
Reward functions are notoriously difficult to specify, especially for tasks with complex goals. Reward learning approaches attempt to infer reward functions from human feedback and…
cs.LG2022★ 2 cited
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…
cs.IT2017
Making compression algorithms for Unicode text
Adam Gleave, Christian Steinruecken
The majority of online content is written in languages other than English, and is most commonly encoded in UTF-8, the world's dominant Unicode character encoding. Traditional compr…