3 papers
cs.LG2026
Learning The Minimum Action Distance
Lorenzo Steccanella, Joshua B. Evans, Ãzgür ÅimÅek +1
This paper presents a state representation framework for Markov decision processes (MDPs) that can be learned solely from state trajectories, requiring neither reward signals nor t…
cs.AI2025
Accelerating Task Generalisation with Multi-Level Skill Hierarchies
Thomas P Cannon, Ãzgür Simsek
Creating reinforcement learning agents that generalise effectively to new tasks is a key challenge in AI research. This paper introduces Fracture Cluster Options (FraCOs), a multi-…
cs.LG2024
Hierarchical Orchestra of Policies
Thomas P Cannon, Ãzgür Simsek
Continual reinforcement learning poses a major challenge due to the tendency of agents to experience catastrophic forgetting when learning sequential tasks. In this paper, we intro…