# Gold Seeker: Information Gain from Policy Distributions for Goal-oriented Vision-and-Langauge Reasoning

## TL;DR

This paper introduces a reinforcement learning method that maintains a distribution over internal information to actively select actions maximizing information gain, improving goal achievement in vision-and-language tasks.

## Contribution

It presents a novel approach that explicitly models information uncertainty and actively chooses actions to reduce it, advancing goal-oriented vision-and-language reasoning.

## Key findings

- Outperforms competitors in visual dialog tasks
- Effectively reduces internal uncertainty in vision-language reasoning
- Demonstrates improved goal achievement in experiments

## Abstract

As Computer Vision moves from a passive analysis of pixels to active analysis of semantics, the breadth of information algorithms need to reason over has expanded significantly. One of the key challenges in this vein is the ability to identify the information required to make a decision, and select an action that will recover it. We propose a reinforcement-learning approach that maintains a distribution over its internal information, thus explicitly representing the ambiguity in what it knows, and needs to know, towards achieving its goal. Potential actions are then generated according to this distribution. For each potential action a distribution of the expected outcomes is calculated, and the value of the potential information gain assessed. The action taken is that which maximizes the potential information gain. We demonstrate this approach applied to two vision-and-language problems that have attracted significant recent interest, visual dialog and visual query generation. In both cases, the method actively selects actions that will best reduce its internal uncertainty and outperforms its competitors in achieving the goal of the challenge.