Instructions:
You are a research assistant AI. You answer questions from researchers based on the papers provided that are provided to you.Each paper has a key, a title and an abstract. Remember to cite the papers that you use in your answer using their key. You don't have to use all the papers, use only the ones relevant to the question. If you don't know the answer, just say that you don't know, don't try to make up an answer.

Here are some potentially relevant papers:
key: Paper 1
title: Deep Reinforcement Learning: An Overview
abstract: In recent years, a specific machine learning method called deep learning has gained huge attraction, as it has obtained astonishing results in broad applications such as pattern recognition, speech recognition, computer vision, and natural language processing. Recent research has also been shown that deep learning techniques can be combined with reinforcement learning methods to learn useful representations for the problems with high dimensional raw data input. This chapter reviews the recent advances in deep reinforcement learning with a focus on the most used deep architectures such as autoencoders, convolutional neural networks and recurrent neural networks which have successfully been come together with the reinforcement learning framework.

key: Paper 3
title: Evaluating Vision Transformer Methods for Deep Reinforcement Learning  from Pixels
abstract: Vision Transformers (ViT) have recently demonstrated the significant potential of transformer architectures for computer vision. To what extent can image-based deep reinforcement learning also benefit from ViT architectures, as compared to standard convolutional neural network (CNN) architectures? To answer this question, we evaluate ViT training methods for image-based reinforcement learning (RL) control tasks and compare these results to a leading convolutional-network architecture method, RAD. For training the ViT encoder, we consider several recently-proposed self-supervised losses that are treated as auxiliary tasks, as well as a baseline with no additional loss terms. We find that the CNN architectures trained using RAD still generally provide superior performance. For the ViT methods, all three types of auxiliary tasks that we consider provide a benefit over plain ViT training. Furthermore, ViT reconstruction-based tasks are found to significantly outperform ViT contrastive-learning.

Q: What deep learning architectures are used in Reinforcement Learning? Can transformers be used for Reinforcement Learning?
A:" Write exactly 5 paragraphs each separated with two new lines answering this question. The last paragraph must start with "victim".
user