# A Survey on Transformers in Reinforcement Learning

arXiv: https://arxiv.org/abs/2301.03044 · HTML: https://mlsanity.com/paper/2301.03044/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- Builds on 111 of its 146 references that are in the map.
- First posted on arXiv 8 Jan 2023.

## Builds on (most cited first, 30 of 111)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [Dream to Control: Learning Behaviors by Latent Imagination](https://mlsanity.com/paper/1912.01603/) (2019)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Off-Policy Deep Reinforcement Learning without Exploration](https://mlsanity.com/paper/1812.02900/) (2018)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [Dota 2 with Large Scale Deep Reinforcement Learning](https://mlsanity.com/paper/1912.06680/) (2019)
- [Inner Monologue: Embodied Reasoning through Planning with Language Models](https://mlsanity.com/paper/2207.05608/) (2022)
- [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022)

## Built on by (most cited first, 30 of 50)

- [A Survey on Vision-Language-Action Models for Embodied AI](https://mlsanity.com/paper/2405.14093/) (2024)
- [Diffusion Models for Reinforcement Learning: A Survey](https://mlsanity.com/paper/2311.01223/) (2023)
- [When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment](https://mlsanity.com/paper/2307.03864/) (2023)
- [Transformers in Speech Processing: A Survey](https://mlsanity.com/paper/2303.11607/) (2023)
- [Learning to Modulate pre-trained Models in RL](https://mlsanity.com/paper/2306.14884/) (2023)
- [Critic-Guided Decision Transformer for Offline Reinforcement Learning](https://mlsanity.com/paper/2312.13716/) (2023)
- [In-Context Reinforcement Learning for Variable Action Spaces](https://mlsanity.com/paper/2312.13327/) (2023)
- [Softmax is 1/2-Lipschitz: A tight bound across all ℓp norms](https://mlsanity.com/paper/2510.23012/) (2025)
- [Jack of All Trades, Master of Some, a Multi-Purpose Transformer Agent](https://mlsanity.com/paper/2402.09844/) (2024)
- [Prompt-Tuning Decision Transformer with Preference Ranking](https://mlsanity.com/paper/2305.09648/) (2023)
- [Recurrent Action Transformer with Memory](https://mlsanity.com/paper/2306.09459/) (2023)
- [Generative Models in Decision Making: A Survey](https://mlsanity.com/paper/2502.17100/) (2025)
- [Vision Transformers for End-to-End Vision-Based Quadrotor Obstacle Avoidance](https://mlsanity.com/paper/2405.10391/) (2024)
- [XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning](https://mlsanity.com/paper/2406.08973/) (2024)
- [Pre-training with Synthetic Data Helps Offline Reinforcement Learning](https://mlsanity.com/paper/2310.00771/) (2023)
- [Think Before You Act: Unified Policy for Interleaving Language Reasoning with Actions](https://mlsanity.com/paper/2304.11063/) (2023)
- [Representation Matters for Mastering Chess: Improved Feature Representation in AlphaZero Outperforms Switching to Transformers](https://mlsanity.com/paper/2304.14918/) (2023)
- [Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning?](https://mlsanity.com/paper/2405.12094/) (2024)
- [From Frege to chatGPT: Compositionality in language, cognition, and deep neural networks](https://mlsanity.com/paper/2405.15164/) (2024)
- [Attention as an RNN](https://mlsanity.com/paper/2405.13956/) (2024)
- [PDiT: Interleaving Perception and Decision-making Transformers for Deep Reinforcement Learning](https://mlsanity.com/paper/2312.15863/) (2023)
- [BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay](https://mlsanity.com/paper/2402.14194/) (2024)
- [Quantum feedback control with a transformer neural network architecture](https://mlsanity.com/paper/2411.19253/) (2024)
- [PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks](https://mlsanity.com/paper/2307.05891/) (2023)
- [Rethinking Layer Relevance in Large Language Models Beyond Cosine Similarity](https://mlsanity.com/paper/2605.14075/) (2026)
- [Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL Approach](https://mlsanity.com/paper/2312.07795/) (2023)
- [Beam Selection in ISAC using Contextual Bandit with Multi-modal Transformer and Transfer Learning](https://mlsanity.com/paper/2503.08937/) (2025)
- [Transformers as Unsupervised Learning Algorithms: A study on Gaussian Mixtures](https://mlsanity.com/paper/2505.11918/) (2025)
- [Odysseus: Scaling VLMs to 100+ Turn Decision-Making in Games via Reinforcement Learning](https://mlsanity.com/paper/2605.00347/) (2026)
- [Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System](https://mlsanity.com/paper/2402.09729/) (2024)

## Abstract (arXiv)

Transformer has been considered the dominating neural architecture in NLP and CV, mostly under supervised settings. Recently, a similar surge of using Transformers has appeared in the domain of reinforcement learning (RL), but it is faced with unique design choices and challenges brought by the nature of RL. However, the evolution of Transformers in RL has not yet been well unraveled. In this paper, we seek to systematically review motivations and progress on using Transformers in RL, provide a taxonomy on existing works, discuss each sub-field, and summarize future prospects.