Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas

Understand

We demonstrate a reinforcement learning agent which uses a compositional recurrent neural network that takes as input an LTL formula and determines satisfying actions.

• The input LTL formulas have never been seen before, yet the network performs zero-shot generalization to satisfy them.
• This is a novel form of multi-task learning for RL agents where agents learn from one diverse set of tasks and generalize to a new set of diverse tasks.
• The formulation of the network enables this capacity to generalize.