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Hierarchical Reinforcement Learning algorithms have successfully been appliedto temporal credit assignment problems with sparse reward signals. However,state-of-the-art algorithms require manual specification of sub-taskstructures, a sample inefficient exploration phase or lack semanticinterpretability. Humans, on the other hand, efficiently detect hierarchicalsub-structures induced by their surroundings. It has been argued that thisinference process universally applies to language, logical reasoning as well asmotor control. Therefore, we propose a cognitive-inspired ReinforcementLearning architecture which uses grammar induction to identify sub-goalpolicies. By treating an on-policy trajectory as a sentence sampled from thepolicy-conditioned language of the environment, we identify hierarchicalconstituents with the help of unsupervised grammatical inference. The resultingset of temporal abstractions is called action grammar (Pastra & Aloimonos,2012) and unifies symbolic and connectionist approaches to ReinforcementLearning. It can be used to facilitate efficient imitation, transfer and onlinelearning.

Semantic RL with Action Grammars: Data-Efficient Learning of Hierarchical Task Abstractions