The Australian Responsible Autonomous Agents Group
A cross-institutional Australian research collective focused on multi-objective reinforcement learning approaches to AI safety and alignment, with researchers at Federation University, Deakin University, and UNSW.
A cross-institutional Australian research collective focused on multi-objective reinforcement learning approaches to AI safety and alignment, with researchers at Federation University, Deakin University, and UNSW.
People
Updated 05/18/26co-leader
co-leader
PhD researcher
researcher
researcher
researcher
intern
researcher
Funding Details
Updated 05/18/26- Annual Budget
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- Current Runway
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- Funding Goal
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- Funding Raised to Date
- $210,000
Org Details
Updated 05/18/26The Australian Responsible Autonomous Agents Collective (ARAAC) is a cross-institutional collective of AI researchers focused on addressing the technical, ethical, and humanitarian challenges posed by the rise of intelligent systems, particularly systems composed of autonomous agents. The collective evolved from the Federation Learning Agents Group (FLAG), a reinforcement learning-focused research group founded by Professor Peter Vamplew at Federation University Australia, which expanded to include members across multiple Australian universities. ARAACis co-led by Professor Peter Vamplew of Federation University Australia (Ballarat) and Professor Richard Dazeley of Deakin University (Geelong). Vamplew has been instrumental since 2008 in establishing multi-objective reinforcement learning (MORL) as a sub-field of reinforcement learning, while Dazeley leads Deakin's Machine Intelligence Lab and specializes in human-alignment of autonomous agents through safe, ethical, explainable, and interactive methods. Both were the first Australians admitted to the Future of Life Institute's Existential AI Safety Research Community in 2022. The collective's research team includes additional researchers and PhD students across Federation University Australia, Deakin University, and the University of New South Wales (UNSW), working on topics including multi-agent systems, fairness and trust in AI, human-robot interaction, human-machine collaboration, and safety knowledge transfer. Notable recent work includes a paper on multi-objective reinforcement learning as a tool for pluralistic alignment, which was accepted with spotlight status (top 3.5% of submissions) at ICML 2024 and presented at the Pluralistic Alignment workshop at NeurIPS 2024. ARAAChas received funding from the Survival and Flourishing Fund ($83,000 in 2023, via Jaan Tallinn) and Founders Pledge ($127,000 in 2024) to support research into multi-objective reinforcement learning applications for AI safety. The Founders Pledge grant funds a research assistant position to develop multi-objective methods for creating safer advanced AI systems. Grants are received through Federation University Australia as the fiscal entity.
Theory of Change
Updated 05/18/26ARAAC believes that conventional reinforcement learning's unconstrained maximization of a single reward or utility measure poses fundamental risks for AI safety and alignment. Their theory of change centers on developing multi-objective reinforcement learning (MORL) methods that use vector rewards instead of scalar rewards, enabling AI systems to simultaneously balance multiple competing objectives such as performance, safety, fairness, and ethical constraints. By treating each aspect of the alignment task as a separate objective, MORL can produce aligned behavior that is difficult or impossible to achieve using scalar reward definitions. This approach supports pluralistic alignment where multiple conflicting values or stakeholders must be considered, and enables methods for automatically learning human preferences and ethics and incorporating them into autonomous agents.
Grants Received
Updated 05/18/26Projects– no linked projects
Updated 05/18/26Discussion
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