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We humans can impeccably search for a target object, given its name only,even in an unseen environment. We argue that this ability is largely due tothree main reasons: the incorporation of prior knowledge (or experience), theadaptation of it to the new environment using the observed visual cues and mostimportantly optimistically searching without giving up early. This is currentlymissing in the state-of-the-art visual navigation methods based onReinforcement Learning (RL). In this paper, we propose to use externallylearned prior knowledge of the relative object locations and integrate it intoour model by constructing a neural graph. In order to efficiently incorporatethe graph without increasing the state-space complexity, we propose ourGraph-based Value Estimation (GVE) module. GVE provides a more accuratebaseline for estimating the Advantage function in actor-critic RL algorithm.This results in reduced value estimation error and, consequently, convergenceto a more optimal policy. Through empirical studies, we show that our agent,dubbed as the optimistic agent, has a more realistic estimate of the statevalue during a navigation episode which leads to a higher success rate. Ourextensive ablation studies show the efficacy of our simple method whichachieves the state-of-the-art results measured by the conventional visualnavigation metrics, e.g. Success Rate (SR) and Success weighted by Path Length(SPL), in AI2THOR environment.

Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation