3 papers
cs.LG2026
Issues with Value-Based Multi-objective Reinforcement Learning: Value Function Interference and Overestimation Sensitivity
Peter Vamplew, Ethan, Watkins +2
Multi-objective reinforcement learning (MORL) algorithms extend conventional reinforcement learning (RL) to the more general case of problems with multiple, conflicting objectives,…
cs.LG2026
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…
cs.LG2025
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…