42 citations · 134 across the 9 of their papers we have counts for
5 papers · 1 filter
Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark
Alexander Pan, Jun Shern Chan, Andy Zou +7
Artificial agents have traditionally been trained to maximize reward, which may incentivize power-seeking and deception, analogous to how next-token prediction in language models (…
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…
An Empirical Investigation of Representation Learning for Imitation
Xin Chen, Sam Toyer, Cody Wild +9
Imitation learning often needs a large demonstration set in order to handle the full range of situations that an agent might find itself in during deployment. However, collecting e…
RvS: What is Essential for Offline RL via Supervised Learning?
Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov +1
Recent work has shown that supervised learning alone, without temporal difference (TD) learning, can be remarkably effective for offline RL. When does this hold true, and which alg…
Sparse Graphical Memory for Robust Planning
Scott Emmons, Ajay Jain, Michael Laskin +3
To operate effectively in the real world, agents should be able to act from high-dimensional raw sensory input such as images and achieve diverse goals across long time-horizons. C…