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

Machine Learning;Artificial Intelligence;Computation and Language