3 papers
cs.AI2025
From Next Token Prediction to (STRIPS) World Models
Carlos Núñez-Molina, Vicenç Gómez, Hector Geffner
We study whether next-token prediction can yield world models that truly support planning, in a controlled symbolic setting where propositional STRIPS action models are learned fro…
cs.LG2024
Hierarchical Average-Reward Linearly-solvable Markov Decision Processes
Guillermo Infante, Anders Jonsson, Vicenç Gómez
We introduce a novel approach to hierarchical reinforcement learning for Linearly-solvable Markov Decision Processes (LMDPs) in the infinite-horizon average-reward setting. Unlike…
cs.LG2024
Planning with a Learned Policy Basis to Optimally Solve Complex Tasks
Guillermo Infante, David Kuric, Anders Jonsson +2
Conventional reinforcement learning (RL) methods can successfully solve a wide range of sequential decision problems. However, learning policies that can generalize predictably acr…