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 the most recent ArXiv preprint 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.
A subset of the full Action Grammars story (defining macro-actions from CFG production rules) also got accepted at the Cognitive Computational Neuroscience conference hosted in Berlin this year! You can find the ArXiv preprint here!