  Conventional deep reinforcement learning methods are sample-inefficient and
usually require a large number of training trials before convergence. Since
such methods operate on an unconstrained action set, they can lead to useless
actions. A recent neuro-symbolic framework called the Logical Neural Networks
(LNNs) can simultaneously provide key-properties of both neural networks and
symbolic logic. The LNNs functions as an end-to-end differentiable network that
minimizes a novel contradiction loss to learn interpretable rules. In this
paper, we utilize LNNs to define an inference graph using basic logical
operations, such as AND and NOT, for faster convergence in reinforcement
learning. Specifically, we propose an integrated method that enables model-free
reinforcement learning from external knowledge sources in an LNNs-based logical
constrained framework such as action shielding and guide. Our results
empirically demonstrate that our method converges faster compared to a
model-free reinforcement learning method that doesn't have such logical
constraints.
