120 citations · 121 across the 3 of their papers we have counts for
5 papers · 1 filter
Multi-objective Reinforcement Learning With Augmented States Requires Rewards After Deployment
Peter Vamplew, Cameron Foale
This research note identifies a previously overlooked distinction between multi-objective reinforcement learning (MORL), and more conventional single-objective reinforcement learni…
ES-C51: Expected Sarsa Based C51 Distributional Reinforcement Learning Algorithm
Rijul Tandon, Peter Vamplew, Cameron Foale
In most value-based reinforcement learning (RL) algorithms, the agent estimates only the expected reward for each action and selects the action with the highest reward. In contrast…
Multi-objective Reinforcement Learning: A Tool for Pluralistic Alignment
Peter Vamplew, Conor F Hayes, Cameron Foale +2
Reinforcement learning (RL) is a valuable tool for the creation of AI systems. However it may be problematic to adequately align RL based on scalar rewards if there are multiple co…
Discrete-to-Deep Supervised Policy Learning
Budi Kurniawan, Peter Vamplew, Michael Papasimeon +2
Neural networks are effective function approximators, but hard to train in the reinforcement learning (RL) context mainly because samples are correlated. For years, scholars have g…
A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitions
Peter Vamplew, Cameron Foale, Richard Dazeley
We report a previously unidentified issue with model-free, value-based approaches to multiobjective reinforcement learning in the context of environments with stochastic state tran…