Reinforcement learning is notoriously sample-inefficient, and one reason why is that agents learn about the world entirely through experience, and it takes lots of experience to learn useful things. One solution you might imagine to this problem is the ones humans by and large use in encountering new environments: instead of learning everything through first-person exploration, acquiring lots of your knowledge by hearing or reading condensed descriptions of the world that can help you take more sensible actions within it. This paper and others like it have the goal of learning RL agents that can take in information about the world in the form of text, and use that information to solve a task. This paper is not the first to propose a solution in this general domain, but it claims to be unique by dint of having both the dynamics of the environment and the goal of the agent change on a per-environment basis, and be described in text. 

The precise details of the architecture used are very much the result of particular engineering done to solve this problem, and as such, it's a bit hard to abstract away generalizable principles that this paper showed, other than the proof of concept fact that tasks of the form they describe - where an agent has to learn which objects can kill which enemies, and pursue the goal of killing certain ones - can be solved. 

Arguably the most central design principle of the paper is aggressive and repeated use of different forms of conditioning architectures, to fully mix the information contained in the textual and visual data streams. This was done in two main ways: 

- Multiple different attention summaries were created, using the document embedding as input, but with queries conditioned on different things (the task, the inventory, a summarized form of the visual features). This is a natural but clever extension of the fact that attention is an easy way to generate conditional aggregated versions of some input
https://i.imgur.com/xIsRu2M.png
- The architecture uses FiLM (Featurewise Linear Modulation), which is essentially a many-generations-generalized version of conditional batch normalization in which the gamma and lambda used to globally shift and scale a feature vector are learned, taking some other data as input. The canonical version of this would be taking in text input, summarizing it into a vector, and then using that vector as input in a MLP that generates gamma and lambda parameters for all of the convolutional layers in a vision system. The interesting innovation of this paper is essentially to argue that this conditioning operation is quite neutral, and that there's no essential way in which the vision input is the "true" data, and the text simply the auxiliary conditioning data: it's more accurate to say that each form of data should conditioning the process of the other one. And so they use Bidirectional FiLM, which does just that, conditioning vision features on text summaries, but also conditioning text features on vision summaries.
https://i.imgur.com/qFaH1k3.png
- The model overall is composed of multiple layers that perform both this mixing FiLM operation, and also visually-conditioned attention. The authors did show, not super surprisingly, that these additional forms of conditioning added performance value to the model relative to the cases where they were ablated