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Peter Vamplew — most-cited papers & profile · Reinforcement Learning
← authors
·
overview
Peter Vamplew
14
papers ·
11
citations ·
26
h-index
Federation University · Sustainability Institute
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Most-cited papers
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey
2021 · 5 citations
Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios
2022 · 2 citations
Elastic Step DQN: A novel multi-step algorithm to alleviate overestimation in Deep QNetworks
2022 · 2 citations
A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitions
2020 · 1 citations
Discrete-to-Deep Supervised Policy Learning
2020 · 1 citations
Multi-objective Reinforcement Learning With Augmented States Requires Rewards After Deployment
2026
Learning the Value Systems of Societies with Preference-based Multi-objective Reinforcement Learning
2026
Ensemble Elastic DQN: A novel multi-step ensemble approach to address overestimation in deep value-based reinforcement learning
2025
On Generalization Across Environments In Multi-Objective Reinforcement Learning
2025
Broad-persistent Advice for Interactive Reinforcement Learning Scenarios
2022
An Empirical Investigation of Value-Based Multi-objective Reinforcement Learning for Stochastic Environments
2024
Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning
2024
Issues with Value-Based Multi-objective Reinforcement Learning: Value Function Interference and Overestimation Sensitivity
2024
Multi-objective Reinforcement Learning: A Tool for Pluralistic Alignment
2024
Top co-authors
Richard Dazeley
· 12
Cameron Foale
· 7
Francisco Cruz
· 4
Adrian Ly
· 2
Sunil Aryal
· 2
Adam Bignold
· 1
Andr\'es Holgado-S\'anchez
· 1
Budi Kurniawan
· 1
Charlotte Young
· 1
Conor F Hayes
· 1
Conor F. Hayes
· 1
Diederik M. Roijers
· 1
Topics
Value-Based
Multi-Agent
Model-Based RL
RLHF & Alignment
Game AI
Meta-RL
Safe RL
Exploration
Policy Gradient