2 citations · 2 across the 4 of their papers we have counts for
4 papers
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning
Peter Vamplew, Cameron Foale, Conor F. Hayes +9
Research in multi-objective reinforcement learning (MORL) has introduced the utility-based paradigm, which makes use of both environmental rewards and a function that defines the u…
An Empirical Investigation of Value-Based Multi-objective Reinforcement Learning for Stochastic Environments
Kewen Ding, Peter Vamplew, Cameron Foale +1
One common approach to solve multi-objective reinforcement learning (MORL) problems is to extend conventional Q-learning by using vector Q-values in combination with a utility func…
Intent-aligned AI systems deplete human agency: the need for agency foundations research in AI safety
Catalin Mitelut, Ben Smith, Peter Vamplew
The rapid advancement of artificial intelligence (AI) systems suggests that artificial general intelligence (AGI) systems may soon arrive. Many researchers are concerned that AIs a…
Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios
Francisco Cruz, Charlotte Young, Richard Dazeley +1
Explainable artificial intelligence is a research field that tries to provide more transparency for autonomous intelligent systems. Explainability has been used, particularly in re…