117 citations · 184 across the 11 of their papers we have counts for
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cs.AI2019
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…
cs.AI2019★ 9 cited
Modeling AGI Safety Frameworks with Causal Influence Diagrams
Tom Everitt, Ramana Kumar, Victoria Krakovna +1
Proposals for safe AGI systems are typically made at the level of frameworks, specifying how the components of the proposed system should be trained and interact with each other. I…