Action Grammars: A Grammar Induction-Based Method for Learning Temporally-Extended Actions

Published in Best (Applied) MAC/MRes/Specialism Project, Sponsored by Winton Capital at Imperial College London, 2018

Recommended citation: Lange, Robert Tjarko. (2018). "Action Grammars: A Grammar Induction-Based Method for Learning Temporally-Extended Actions." Imperial College London - DoC - Best (Applied) MAC/MRes/Specialism Project 2018.

Download paper here.

This working paper is the result of my Masters project at Imperial College London supervised by Aldo Faisal. We combine tools from grammatical inference in order to learn a grammar of actions. A Hierarchical Reinforcement Learning agent can then utilize the resulting temporally-extended actions in order to combat the curse of dimensionality in sparse reward environments.

The thesis has won the ‘Best (Applied) MAC/MRes/Specialism Project, Sponsored by Winton Capital at Imperial College London’ prize at the Department of Computing.