Learning Symbolic Rules for Interpretable Deep Reinforcement Learning
  Recent progress in deep reinforcement learning (DRL) can be largely
attributed to the use of neural networks. However, this black-box approach
fails to explain the learned policy in a human understandable way. To address
this challenge and improve the transparency, we propose a Neural Symbolic
Reinforcement Learning framework by introducing symbolic logic into DRL. This
framework features a fertilization of reasoning and learning modules, enabling
end-to-end learning with prior symbolic knowledge. Moreover, interpretability
is achieved by extracting the logical rules learned by the reasoning module in
a symbolic rule space. The experimental results show that our framework has
better interpretability, along with competing performance in comparison to
state-of-the-art approaches.
