Learning POMDPs beyond full-rank actions and state observability

An updated arXiv paper tackles how autonomous agents can model systems whose true state is hidden, such as devices with locking mechanisms. The authors frame the problem as parameter learning for discrete partially observable Markov decision processes and extend the approach to cases where the usual full-rank assumptions on actions and state observations do not hold. The work is cross-listed in arXiv's AI and machine learning categories.