831 citations · 3.2k across the 65 of their papers we have counts for
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Multi-Principal Assistance Games: Definition and Collegial Mechanisms
Arnaud Fickinger, Simon Zhuang, Andrew Critch +2
We introduce the concept of a multi-principal assistance game (MPAG), and circumvent an obstacle in social choice theory, Gibbard's theorem, by using a sufficiently collegial prefe…
Understanding Learned Reward Functions
Eric J. Michaud, Adam Gleave, Stuart Russell
In many real-world tasks, it is not possible to procedurally specify an RL agent's reward function. In such cases, a reward function must instead be learned from interacting with a…
DERAIL: Diagnostic Environments for Reward And Imitation Learning
Pedro Freire, Adam Gleave, Sam Toyer +1
The objective of many real-world tasks is complex and difficult to procedurally specify. This makes it necessary to use reward or imitation learning algorithms to infer a reward or…
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
Michael Dennis, Natasha Jaques, Eugene Vinitsky +4
A wide range of reinforcement learning (RL) problems - including robustness, transfer learning, unsupervised RL, and emergent complexity - require specifying a distribution of task…
The MAGICAL Benchmark for Robust Imitation
Sam Toyer, Rohin Shah, Andrew Critch +1
Imitation Learning (IL) algorithms are typically evaluated in the same environment that was used to create demonstrations. This rewards precise reproduction of demonstrations in on…
SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory
Paria Rashidinejad, Jiantao Jiao, Stuart Russell
We present an efficient and practical (polynomial time) algorithm for online prediction in unknown and partially observed linear dynamical systems (LDS) under stochastic noise. Whe…