Reinforcement Learning with External Knowledge by using Logical Neural Networks

Understand

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.