  Deep reinforcement learning (RL) methods often require many trials before
convergence, and no direct interpretability of trained policies is provided. In
order to achieve fast convergence and interpretability for the policy in RL, we
propose a novel RL method for text-based games with a recent neuro-symbolic
framework called Logical Neural Network, which can learn symbolic and
interpretable rules in their differentiable network. The method is first to
extract first-order logical facts from text observation and external word
meaning network (ConceptNet), then train a policy in the network with directly
interpretable logical operators. Our experimental results show RL training with
the proposed method converges significantly faster than other state-of-the-art
neuro-symbolic methods in a TextWorld benchmark.
