Verifiable RNN-Based Policies for POMDPs Under Temporal Logic
  Constraints
  Recurrent neural networks (RNNs) have emerged as an effective representation
of control policies in sequential decision-making problems. However, a major
drawback in the application of RNN-based policies is the difficulty in
providing formal guarantees on the satisfaction of behavioral specifications,
e.g. safety and/or reachability. By integrating techniques from formal methods
and machine learning, we propose an approach to automatically extract a
finite-state controller (FSC) from an RNN, which, when composed with a
finite-state system model, is amenable to existing formal verification tools.
Specifically, we introduce an iterative modification to the so-called quantized
bottleneck insertion technique to create an FSC as a randomized policy with
memory. For the cases in which the resulting FSC fails to satisfy the
specification, verification generates diagnostic information. We utilize this
information to either adjust the amount of memory in the extracted FSC or
perform focused retraining of the RNN. While generally applicable, we detail
the resulting iterative procedure in the context of policy synthesis for
partially observable Markov decision processes (POMDPs), which is known to be
notoriously hard. The numerical experiments show that the proposed approach
outperforms traditional POMDP synthesis methods by 3 orders of magnitude within
2% of optimal benchmark values.
