398 citations · 1.8k across the 163 of their papers we have counts for
6 papers · 1 filter
Gated Linear Networks
Joel Veness, Tor Lattimore, David Budden +8
This paper presents a new family of backpropagation-free neural architectures, Gated Linear Networks (GLNs). What distinguishes GLNs from contemporary neural networks is the distri…
Reward Tampering Problems and Solutions in Reinforcement Learning: A Causal Influence Diagram Perspective
Tom Everitt, Marcus Hutter, Ramana Kumar +1
Can humans get arbitrarily capable reinforcement learning (RL) agents to do their bidding? Or will sufficiently capable RL agents always find ways to bypass their intended objectiv…
Fairness without Regret
Marcus Hutter
A popular approach of achieving fairness in optimization problems is by constraining the solution space to "fair" solutions, which unfortunately typically reduces solution quality.…
Conditions on Features for Temporal Difference-Like Methods to Converge
Marcus Hutter, Samuel Yang-Zhao, Sultan J. Majeed
The convergence of many reinforcement learning (RL) algorithms with linear function approximation has been investigated extensively but most proofs assume that these methods conver…
Asymptotically Unambitious Artificial General Intelligence
Michael K Cohen, Badri Vellambi, Marcus Hutter
General intelligence, the ability to solve arbitrary solvable problems, is supposed by many to be artificially constructible. Narrow intelligence, the ability to solve a given part…
A Strongly Asymptotically Optimal Agent in General Environments
Michael K. Cohen, Elliot Catt, Marcus Hutter
Reinforcement Learning agents are expected to eventually perform well. Typically, this takes the form of a guarantee about the asymptotic behavior of an algorithm given some assump…