1 citations · 1 across the 3 of their papers we have counts for
4 papers
The Problem of Social Cost in Multi-Agent General Reinforcement Learning: Survey and Synthesis
Kee Siong Ng, Samuel Yang-Zhao, Timothy Cadogan-Cowper
The AI safety literature is full of examples of powerful AI agents that, in blindly pursuing a specific and usually narrow objective, ends up with unacceptable and even catastrophi…
Privacy Preserving Reinforcement Learning for Population Processes
Samuel Yang-Zhao, Kee Siong Ng
We consider the problem of privacy protection in Reinforcement Learning (RL) algorithms that operate over population processes, a practical but understudied setting that includes,…
Dynamic Knowledge Injection for AIXI Agents
Samuel Yang-Zhao, Kee Siong Ng, Marcus Hutter
Prior approximations of AIXI, a Bayesian optimality notion for general reinforcement learning, can only approximate AIXI's Bayesian environment model using an a-priori defined set…
A Monte Carlo AIXI Approximation
Joel Veness, Kee Siong Ng, Marcus Hutter +2
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. Our approach is based on a direct approximation of AIXI, a Bayesian o…