# Critic-Guided Decision Transformer for Offline Reinforcement Learning

arXiv: https://arxiv.org/abs/2312.13716 · HTML: https://mlsanity.com/paper/2312.13716/

## Key facts

- Cited by 26 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 24 of its 29 references that are in the map.
- First posted on arXiv 21 Dec 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, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Off-Policy Deep Reinforcement Learning without Exploration](https://mlsanity.com/paper/1812.02900/) (2018)
- [Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction](https://mlsanity.com/paper/1906.00949/) (2019)
- [Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning](https://mlsanity.com/paper/1910.00177/) (2019)
- [Large Batch Optimization for Deep Learning: Training BERT in 76 minutes](https://mlsanity.com/paper/1904.00962/) (2019)
- [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://mlsanity.com/paper/2106.02039/) (2021)
- [Critic Regularized Regression](https://mlsanity.com/paper/2006.15134/) (2020)
- [Offline Reinforcement Learning with Fisher Divergence Critic Regularization](https://mlsanity.com/paper/2103.08050/) (2021)
- [RvS: What is Essential for Offline RL via Supervised Learning?](https://mlsanity.com/paper/2112.10751/) (2021)
- [Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to Actions](https://mlsanity.com/paper/1912.02875/) (2019)
- [Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization](https://mlsanity.com/paper/2303.15810/) (2023)
- [Generalized Decision Transformer for Offline Hindsight Information Matching](https://mlsanity.com/paper/2111.10364/) (2021)
- [Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL](https://mlsanity.com/paper/2209.03993/) (2022)
- [Reward-Conditioned Policies](https://mlsanity.com/paper/1912.13465/) (2019)
- [BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning](https://mlsanity.com/paper/1910.12179/) (2019)
- [You Can't Count on Luck: Why Decision Transformers and RvS Fail in Stochastic Environments](https://mlsanity.com/paper/2205.15967/) (2022)
- [A Survey on Transformers in Reinforcement Learning](https://mlsanity.com/paper/2301.03044/) (2023)
- [Dichotomy of Control: Separating What You Can Control from What You Cannot](https://mlsanity.com/paper/2210.13435/) (2022)
- [Large Sequence Models for Sequential Decision-Making: A Survey](https://mlsanity.com/paper/2306.13945/) (2023)
- [How Crucial is Transformer in Decision Transformer?](https://mlsanity.com/paper/2211.14655/) (2022)

## Built on by (most cited first, 26 of 26)

- [Generative Trajectory Stitching through Diffusion Composition](https://mlsanity.com/paper/2503.05153/) (2025)
- [Q-value Regularized Transformer for Offline Reinforcement Learning](https://mlsanity.com/paper/2405.17098/) (2024)
- [Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions](https://mlsanity.com/paper/2402.13777/) (2024)
- [Decision Mamba: A Multi-Grained State Space Model with Self-Evolution Regularization for Offline RL](https://mlsanity.com/paper/2406.05427/) (2024)
- [Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning](https://mlsanity.com/paper/2402.17217/) (2024)
- [Adaptive Q-Aid for Conditional Supervised Learning in Offline Reinforcement Learning](https://mlsanity.com/paper/2402.02017/) (2024)
- [Autoformalizer with Tool Feedback](https://mlsanity.com/paper/2510.06857/) (2025)
- [Solving Continual Offline RL through Selective Weights Activation on Aligned Spaces](https://mlsanity.com/paper/2410.15698/) (2024)
- [Return-Aligned Decision Transformer](https://mlsanity.com/paper/2402.03923/) (2024)
- [Continual Diffuser (CoD): Mastering Continual Offline Reinforcement Learning with Experience Rehearsal](https://mlsanity.com/paper/2409.02512/) (2024)
- [Decision SpikeFormer: Spike-Driven Transformer for Decision Making](https://mlsanity.com/paper/2504.03800/) (2025)
- [Decoupling Return-to-Go for Efficient Decision Transformer](https://mlsanity.com/paper/2601.15953/) (2026)
- [Controllable User Simulation](https://mlsanity.com/paper/2605.11519/) (2026)
- [TOUR: A Trajectory-Level Unlearning Benchmark for Offline Reinforcement Learning](https://mlsanity.com/paper/2607.21111/) (2026)
- [Decision MetaMamba: Enhancing Selective SSM in Offline RL with Heterogeneous Sequence Mixing](https://mlsanity.com/paper/2408.10517/) (2024)
- [Mildly Conservative Regularized Evaluation for Offline Reinforcement Learning](https://mlsanity.com/paper/2508.05960/) (2025)
- [Adjusting the Output of Decision Transformer with Action Gradient](https://mlsanity.com/paper/2510.05285/) (2025)
- [Target-Aligned Fusion for Decision-Sequence Learning under Dynamics Shift](https://mlsanity.com/paper/2511.09173/) (2025)
- [A Comparison Between Decision Transformers and Traditional Offline Reinforcement Learning Algorithms](https://mlsanity.com/paper/2511.16475/) (2025)
- [Conditional Sequence Modeling for Safe Reinforcement Learning](https://mlsanity.com/paper/2602.08584/) (2026)
- [Decision MetaMamba: Enhancing Selective SSM in Offline RL with Heterogeneous Sequence Mixing](https://mlsanity.com/paper/2602.19805/) (2026)
- [Selecting Offline Reinforcement Learning Algorithms for Stochastic Network Control](https://mlsanity.com/paper/2603.03932/) (2026)
- [Meta-Adaptive Beam Search Planning for Transformer-Based Reinforcement Learning Control of UAVs with Overhead Manipulators under Flight Disturbances](https://mlsanity.com/paper/2603.26612/) (2026)
- [Beyond Autoregressive RTG: Conditioning via Injection Outside Sequential Modeling in Decision Transformer](https://mlsanity.com/paper/2605.06104/) (2026)
- [Return-to-Go Is More Than a Number: Q-Guided Alignment for Return-Conditioned Supervised Learning](https://mlsanity.com/paper/2605.29028/) (2026)
- [Trust Guided Decision Transformer](https://mlsanity.com/paper/2609.31586/) (2026)

## Abstract (arXiv)

Recent advancements in offline reinforcement learning (RL) have underscored the capabilities of Return-Conditioned Supervised Learning (RCSL), a paradigm that learns the action distribution based on target returns for each state in a supervised manner. However, prevailing RCSL methods largely focus on deterministic trajectory modeling, disregarding stochastic state transitions and the diversity of future trajectory distributions. A fundamental challenge arises from the inconsistency between the sampled returns within individual trajectories and the expected returns across multiple trajectories. Fortunately, value-based methods offer a solution by leveraging a value function to approximate the expected returns, thereby addressing the inconsistency effectively. Building upon these insights, we propose a novel approach, termed the Critic-Guided Decision Transformer (CGDT), which combines the predictability of long-term returns from value-based methods with the trajectory modeling capability of the Decision Transformer. By incorporating a learned value function, known as the critic, CGDT ensures a direct alignment between the specified target returns and the expected returns of actions. This integration bridges the gap between the deterministic nature of RCSL and the probabilistic characteristics of value-based methods. Empirical evaluations on stochastic environments and D4RL benchmark datasets demonstrate the superiority of CGDT over traditional RCSL methods. These results highlight the potential of CGDT to advance the state of the art in offline RL and extend the applicability of RCSL to a wide range of RL tasks.