Portrait of David Scott Krueger is unavailable

David Scott Krueger

Core Academic Member
kruegerd@mila.quebec
Assistant professor, Université de Montréal, Department of Computer Science and Operations Research (DIRO)

Biography

David Krueger is an Assistant Professor in Robust, Reasoning and Responsible AI in the Department of Computer Science and Operations Research (DIRO) at University of Montreal, and a Core Academic Member at Mila - Quebec Artificial Intelligence Institute, UC Berkeley's Center for Human-Compatible AI (CHAI), and the Center for the Study of Existential Risk (CSER). His work focuses on reducing the risk of human extinction from artificial intelligence (AI x-risk) through technical research as well as education, outreach, governance and advocacy.

His research spans many areas of Deep Learning, AI Alignment, AI Safety and AI Ethics, including alignment failure modes, algorithmic manipulation, interpretability, robustness, and understanding how AI systems learn and generalize. He has been featured in media outlets including ITV's Good Morning Britain, Al Jazeera's Inside Story, France 24, New Scientist and the Associated Press.

David completed his graduate studies at the University of Montreal and Mila - Quebec Artificial Intelligence Institute, working with Yoshua Bengio, Roland Memisevic, and Aaron Courville.

Current Students

Alan Chan
PhD - Université de Montréal
Principal supervisor :
Nicolas Le Roux
alan.chan@mila.quebec
Website

Publications

On The Fragility of Learned Reward Functions
Lev E McKinney
Yawen Duan
David Scott Krueger
Adam Gleave
Reward functions are notoriously difficult to specify, especially for tasks with complex goals. Reward learning approaches attempt to infer … (see more)reward functions from human feedback and preferences. Prior works on reward learning have mainly focused on the performance of policies trained alongside the reward function. This practice, however, may fail to detect learned rewards that are not capable of training new policies from scratch and thus do not capture the intended behavior. Our work focuses on demonstrating and studying the causes of these relearning failures in the domain of preference-based reward learning. We demonstrate with experiments in tabular and continuous control environments that the severity of relearning failures can be sensitive to changes in reward model design and the trajectory dataset composition. Based on our findings, we emphasize the need for more retraining-based evaluations in the literature.
2022-12-09
NeurIPS.cc/2022/Workshop/DeepRL (unknown)
doi.org
openreview.net
Training Equilibria in Reinforcement Learning
Lauro Langosco
David Scott Krueger
Adam Gleave
In partially observable environments, reinforcement learning algorithms such as policy gradient and Q-learning may have multiple equilibria-… (see more)--policies that are stable under further training---and can converge to equilibria that are strictly suboptimal. Prior work blames insufficient exploration, but suboptimal equilibria can arise despite full exploration and other favorable circumstances like a flexible policy parametrization. We show theoretically that the core problem is that in partially observed environments, an agent's past actions induce a distribution on hidden states. Equipping the policy with memory helps it model the hidden state and leads to convergence to a higher reward equilibrium, \emph{even when there exists a memoryless optimal policy}. Experiments show that policies with insufficient memory tend to learn to use the environment as auxiliary memory, and parameter noise helps policies escape suboptimal equilibria.
2022-12-09
NeurIPS.cc/2022/Workshop/DeepRL (unknown)
openreview.net
openreview.net