This paper introduces the QMDP-net, a neural network architecture for planning under partial observability. The QMDP-net combines the strengths of model-free learning and model-based planning. It is a recurrent policy network, but it represents a policy by connecting a model with a planning algorithm that solves the model, thus embedding the solution structure of planning in a network learning architecture. The QMDP-net is fully differentiable and allows end-to-end training. We train a QMDP-net in a set of different environments so that it can generalize over new ones and "transfer" to larger environments as well. In preliminary experiments, QMDP-net showed strong performance on several robotic tasks in simulation. Interestingly, while QMDP-net encodes the QMDP algorithm, it sometimes outperforms the QMDP algorithm in the experiments, because of QMDP-net's increased robustness through end-to-end learning.
In planning problems, REF have proposed recently QMDP-Net combining POMDP and LSTM to obtain a neural network architecture under partial observability.
QMDP-Net: Deep Learning for Planning under Partial Observability
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