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cs.LG2026
Compositional Planning with Jumpy World Models
Jesse Farebrother, Matteo Pirotta, Andrea Tirinzoni +3
The ability to plan with temporal abstractions is central to intelligent decision-making. Rather than reasoning over primitive actions, we study agents that compose pre-trained pol…
cs.LG2024
Reinforcement Learning with Options and State Representation
Ayoub Ghriss, Masashi Sugiyama, Alessandro Lazaric
The current thesis aims to explore the reinforcement learning field and build on existing methods to produce improved ones to tackle the problem of learning in high-dimensional and…