Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas
Name
CBMM-Memo-125.pdf
Size
2.12 MB
Format
Adobe PDF
Checksum (MD5)
f3480ebd99a8e740490bcb46d4d8d0b8
Author(s)
Kuo, Yen-Ling
Katz, Boris
Barbu, Andrei
Date Issued
October 25, 2020
Publisher
Center for Brains, Minds and Machines (CBMM), The Ninth International Conference on Learning Representations (ICLR)
Series/Report no.
CBMM Memo;125
Abstract
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. We demonstrate this ability in two domains. In a symbolic domain, the agent finds a sequence of letters that is accepted. In a Minecraft-like environment, the agent finds a sequence of actions that conform to the formula. While prior work could learn to execute one formula reliably given examples of that formula, we demonstrate how to encode all formulas reliably. This could form the basis of new multi- task agents that discover sub-tasks and execute them without any additional training, as well as the agents which follow more complex linguistic commands. The structures required for this generalization are specific to LTL formulas, which opens up an interesting theoretical question: what structures are required in neural networks for zero-shot generalization to different logics?
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