# Rethinking Decision Transformer via Hierarchical Reinforcement Learning

arXiv: https://arxiv.org/abs/2311.00267 · HTML: https://mlsanity.com/paper/2311.00267/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Decision transformers and sequence modeling, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 45 of its 54 references that are in the map.
- First posted on arXiv 1 Nov 2023.

## Placed in

- Offline RL and imitation learning from data / [Decision transformers and sequence modeling](https://mlsanity.com/offlinerl/decision-transformers-and-sequence-modeling/)

## Builds on (most cited first, 30 of 45)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [D4RL: Datasets for Deep Data-Driven Reinforcement Learning](https://mlsanity.com/paper/2004.07219/) (2020)
- [Offline Reinforcement Learning with Implicit Q-Learning](https://mlsanity.com/paper/2110.06169/) (2021)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [Off-Policy Deep Reinforcement Learning without Exploration](https://mlsanity.com/paper/1812.02900/) (2018)
- [A Minimalist Approach to Offline Reinforcement Learning](https://mlsanity.com/paper/2106.06860/) (2021)
- [A Generalist Agent](https://mlsanity.com/paper/2205.06175/) (2022)
- [Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning](https://mlsanity.com/paper/1910.00177/) (2019)
- [What Can Transformers Learn In-Context? A Case Study of Simple Function Classes](https://mlsanity.com/paper/2208.01066/) (2022)
- [Automatic Chain of Thought Prompting in Large Language Models](https://mlsanity.com/paper/2210.03493/) (2022)
- [What learning algorithm is in-context learning? Investigations with linear models](https://mlsanity.com/paper/2211.15661/) (2022)
- [Data-Efficient Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1805.08296/) (2018)
- [The Option-Critic Architecture](https://mlsanity.com/paper/1609.05140/) (2016)
- [In-context Reinforcement Learning with Algorithm Distillation](https://mlsanity.com/paper/2210.14215/) (2022)
- [RvS: What is Essential for Offline RL via Supervised Learning?](https://mlsanity.com/paper/2112.10751/) (2021)
- [OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning](https://mlsanity.com/paper/2010.13611/) (2020)
- [Chain of Hindsight Aligns Language Models with Feedback](https://mlsanity.com/paper/2302.02676/) (2023)

## Built on by (most cited first, 14 of 14)

- [Decision Mamba: A Multi-Grained State Space Model with Self-Evolution Regularization for Offline RL](https://mlsanity.com/paper/2406.05427/) (2024)
- [Pretraining Decision Transformers with Reward Prediction for In-Context Multi-task Structured Bandit Learning](https://mlsanity.com/paper/2406.05064/) (2024)
- [Reinforcement Learning Gradients as Vitamin for Online Finetuning Decision Transformers](https://mlsanity.com/paper/2410.24108/) (2024)
- [PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-Performer](https://mlsanity.com/paper/2406.06793/) (2024)
- [Navigation with QPHIL: Quantizing Planner for Hierarchical Implicit Q-Learning](https://mlsanity.com/paper/2411.07760/) (2024)
- [Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning](https://mlsanity.com/paper/2506.12529/) (2025)
- [Adaptive Neighborhood-Constrained Q Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2511.02567/) (2025)
- [SANDWICH: Towards an Offline, Differentiable, Fully-Trainable Wireless Neural Ray-Tracing Surrogate](https://mlsanity.com/paper/2411.08767/) (2024)
- [Decision MetaMamba: Enhancing Selective SSM in Offline RL with Heterogeneous Sequence Mixing](https://mlsanity.com/paper/2408.10517/) (2024)
- [Adjusting the Output of Decision Transformer with Action Gradient](https://mlsanity.com/paper/2510.05285/) (2025)
- [Robust Adversarial Reinforcement Learning in Stochastic Games via Sequence Modeling](https://mlsanity.com/paper/2510.11877/) (2025)
- [GAS: Enhancing Reward-Cost Balance of Generative Model-assisted Offline Safe RL](https://mlsanity.com/paper/2602.05323/) (2026)
- [Decision MetaMamba: Enhancing Selective SSM in Offline RL with Heterogeneous Sequence Mixing](https://mlsanity.com/paper/2602.19805/) (2026)
- [Beyond Autoregressive RTG: Conditioning via Injection Outside Sequential Modeling in Decision Transformer](https://mlsanity.com/paper/2605.06104/) (2026)

## Abstract (arXiv)

Decision Transformer (DT) is an innovative algorithm leveraging recent advances of the transformer architecture in reinforcement learning (RL). However, a notable limitation of DT is its reliance on recalling trajectories from datasets, losing the capability to seamlessly stitch sub-optimal trajectories together. In this work we introduce a general sequence modeling framework for studying sequential decision making through the lens of Hierarchical RL. At the time of making decisions, a high-level policy first proposes an ideal prompt for the current state, a low-level policy subsequently generates an action conditioned on the given prompt. We show DT emerges as a special case of this framework with certain choices of high-level and low-level policies, and discuss the potential failure of these choices. Inspired by these observations, we study how to jointly optimize the high-level and low-level policies to enable the stitching ability, which further leads to the development of new offline RL algorithms. Our empirical results clearly show that the proposed algorithms significantly surpass DT on several control and navigation benchmarks. We hope our contributions can inspire the integration of transformer architectures within the field of RL.