Feudal Reinforcement Learning by Reading Manuals

TL;DR

This paper introduces a Feudal Reinforcement Learning model with a manager-worker structure that improves reasoning from manuals by generating multi-hop plans, achieving strong results without manual curriculum design.

Contribution

The paper proposes a novel FRL framework with a multi-hop plan generator that better aligns high-level reasoning with low-level actions, applicable across diverse environments.

Findings

Achieves competitive performance on RTFM and Messenger tasks.

Effectively alleviates semantic mismatch between instructions and actions.

Does not require human-designed curriculum learning.

Abstract

Reading to act is a prevalent but challenging task which requires the ability to reason from a concise instruction. However, previous works face the semantic mismatch between the low-level actions and the high-level language descriptions and require the human-designed curriculum to work properly. In this paper, we present a Feudal Reinforcement Learning (FRL) model consisting of a manager agent and a worker agent. The manager agent is a multi-hop plan generator dealing with high-level abstract information and generating a series of sub-goals in a backward manner. The worker agent deals with the low-level perceptions and actions to achieve the sub-goals one by one. In comparison, our FRL model effectively alleviate the mismatching between text-level inference and low-level perceptions and actions; and is general to various forms of environments, instructions and manuals; and our multi-hop plan generator can significantly boost for challenging tasks where multi-step reasoning form the texts is critical to resolve the instructed goals. We showcase our approach achieves competitive performance on two challenging tasks, Read to Fight Monsters (RTFM) and Messenger, without human-designed curriculum learning.