Professor Peter Vamplew
Federation University Australia
Peter Vamplew is a Professor of Information Technology at Federation University (Ballarat), and co-founder/co-leader of ARAAC. Previously he has been Associate Dean (Research) for Science and Technology at Federation University. Since 2008 he has played a leading role in establishing multi-objective reinforcement learning (MORL) as a sub-field of reinforcement learning, explicitly designed for problems with multiple conflicting objectives (which describes most real-world problems). More recently he has been studying the role which MORL can play in creating human-aligned artificial intelligence. His contributions in this area are reflected by his appointment as a senior member of the Future of Life Institute’s Existential AI safety Research Community. He has been an associate editor for Neurocomputing journal, and guest editor of special issues of Neurocomputing and JAAMAS on MORL and multi-objective decision-making, and an invited speaker at the Adaptive Learning Agents workshop and the Workshop on Human-aligned Reinforcement Learning for Autonomous Agents and Robots. He is also an experienced supervisor of Masters and PhD research students.
ARAAC Publications
- (2025). AI apology: a critical review of apology in AI systems Artificial Intelligence Review
- (2025). An empirical investigation of value-based multi-objective reinforcement learning for stochastic environments The Knowledge Engineering Review
- (2024). Elastic step DQN: A novel multi-step algorithm to alleviate overestimation in Deep Q-Networks Neurocomputing
- (2024). Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning International Conference on Autonomous Agents and Multiagent Systems (AAMAS)
- (2023). Elastic step DDPG: Multi-step reinforcement learning for improved sample efficiency International Joint Conference on Neural Networks (IJCNN)
- (2023). A NetHack Learning Environment Language Wrapper for Autonomous Agents Journal of Open Research Software
- (2023). Human engagement providing evaluative and informative advice for interactive reinforcement learning Neural Computing and Applications
- (2023). Persistent rule-based interactive reinforcement learning Neural Computing and Applications
- (2023). Explainable reinforcement learning for broad-XAI: a conceptual framework and survey Neural Computing and Applications
- (2023). AI apology: interactive multi-objective reinforcement learning for human-aligned AI Neural Computing and Applications
- (2022). Evaluating human-like explanations for robot actions in reinforcement learning scenarios IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- (2022). The impact of environmental stochasticity on value-based multiobjective reinforcement learning Neural Computing and Applications
- (2022). A practical guide to multi-objective reinforcement learning and planning Autonomous Agents and Multi-Agent Systems (JAAMAS)
- (2022). Scalar reward is not enough: a response to Silver, Singh, Precup and Sutton (2021) Autonomous Agents and Multi-Agent Systems (JAAMAS)
- (2021). Language Representations for Generalization in Reinforcement Learning Asian Conference on Machine Learning (ACML)
- (2021). An evaluation methodology for interactive reinforcement learning with simulated users Biomimetics
- (2021). A conceptual framework for externally-influenced agents: an assisted reinforcement learning review Journal of Ambient Intelligence and Humanized Computing
- (2021). Explainable robotic systems: understanding goal-driven actions in a reinforcement learning scenario Neural Computing and Applications
- (2021). Levels of explainable artificial intelligence for human-aligned conversational explanations Artificial Intelligence
- (2021). Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety Engineering Applications of Artificial Intelligence
- (2020). A multi-objective deep reinforcement learning framework Engineering Applications of Artificial Intelligence
- (2019). Memory-based explainable reinforcement learning Australasian Joint Conference on Artificial Intelligence (AI 2019)
- (2018). Human-aligned artificial intelligence is a multiobjective problem Ethics and Information Technology
- (2017). Steering approaches to Pareto-optimal multiobjective reinforcement learning Neurocomputing
- (2017). Softmax exploration strategies for multiobjective reinforcement learning Neurocomputing
- (2013). A survey of multi-objective sequential decision-making Journal of Artificial Intelligence Research