4 papers
Robust Successor Features
Erik Nikulski, Yamen Habib, Vicenç Gomez +3
Generalization in Reinforcement Learning (RL) refers to the ability to execute close-to-optimal policies in unseen tasks after the agent has been trained on a different set of task…
Intermediate Results on the Complexity of STRIPS
Stefan Edelkamp, Jiří Fink, Petr Gregor +2
This paper is based on Bylander's results on the computational complexity of propositional STRIPS planning. He showed that when only ground literals are permitted, determining plan…
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…
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…