Professor Richard Dazeley
Deakin University
Richard Dazeley is an Associate Professor of Computer Science at Deakin University (Geelong), Leader of the Machine Intelligence Lab and the Deputy Head of School. He is a leading researcher in the Human-alignment of autonomous agents through Safe, Ethical, Explainable and Interactive methods utilising Multiobjective Reinforcement Learning (MORL) and is a senior member of the AI existential Safety Community.
He has been an invited speaker at Monash University’s Advanced Seminar on Agents and Decision-Making, the Workshop on Human-aligned Reinforcement Learning for Autonomous Agents and Robots. He has also been a guest editor for Multi-Objective Decision Making (MODeM 2017, 2021) and on the special issue on Human-aligned Reinforcement Learning in Neural Computing and Applications. He was a member of the IEEE P7001 Transparency of Autonomous Systems working group and has organised and served on numerous program committees for many leading conferences.
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). Towards a broad-persistent advising approach for deep interactive reinforcement learning in robotic environments Sensors
- (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). Human-aligned reinforcement learning for autonomous agents and robots 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). A robust approach for continuous interactive actor-critic algorithms IEEE Access
- (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). Deep reinforcement learning with interactive feedback in a human-robot environment Applied Sciences
- (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