# Mapping Instructions and Visual Observations to Actions with Reinforcement Learning

## TL;DR

This paper introduces a reinforcement learning approach that maps raw visual and textual inputs directly to actions for instruction execution, eliminating the need for structured representations or multiple models.

## Contribution

It presents a unified model trained with reward shaping in a reinforcement learning framework to jointly interpret visual and linguistic inputs for action prediction.

## Key findings

- Significant improvements over supervised learning methods
- Effective exploration guided by reward shaping
- No need for intermediate representations or multiple models

## Abstract

We propose to directly map raw visual observations and text input to actions for instruction execution. While existing approaches assume access to structured environment representations or use a pipeline of separately trained models, we learn a single model to jointly reason about linguistic and visual input. We use reinforcement learning in a contextual bandit setting to train a neural network agent. To guide the agent's exploration, we use reward shaping with different forms of supervision. Our approach does not require intermediate representations, planning procedures, or training different models. We evaluate in a simulated environment, and show significant improvements over supervised learning and common reinforcement learning variants.