Reinforcement Learning

Invited keynote at RECODE 2026 (Research in Computing and Software Development), the University of Johannesburg’s 2nd Symposium for Master’s and Doctoral Students, held alongside the 40th ACSSE Projects Day.

Reinforcement Learning (RL) research often models environments as Markov decision processes. Yet many real-world tasks are …

Invited talk as part of the Early Career Spotlight (ECS) program at IJCAI-ECAI 2026, which highlights early-career researchers who have made significant contributions to AI.

We describe a research programme aimed at the construction of a single, generally intelligent agent—one competent across all …

Safe offline RL typically assumes access to dense per-step cost annotations, but in practice supervisors provide only trajectory-level …

Reinforcement learning is often framed around episodic, discounted, or average scalar rewards. While useful, these views miss a core …

An important problem in reinforcement learning is designing agents that learn to solve tasks safely in an environment. A common …

One avenue for creating a generally intelligent agent is to equip it with the ability to combine its previously learned behaviours to …

Evaluating moral alignment in agents navigating conflicting, hierarchically structured human norms is a critical challenge at the …

We consider how to construct state abstractions compatible with a given set of abstract actions, to obtain a well-formed abstract …