1 citations · 2 across the 5 of their papers we have counts for
6 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 Direct Approximation of AIXI Using Logical State Abstractions
Samuel Yang-Zhao, Tianyu Wang, Kee Siong Ng
We propose a practical integration of logical state abstraction with AIXI, a Bayesian optimality notion for reinforcement learning agents, to significantly expand the model class t…
Belief Acquisition as Stochastic Filtering
Dawei Chen, John Lloyd, Samuel Yang-Zhao +1
This paper studies how belief acquisition can be accomplished using stochastic filtering. First, a theoretical foundation for empirical beliefs is outlined. Then stochastic filteri…
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…