# Constrained Decision Transformer for Offline Safe Reinforcement Learning

arXiv: https://arxiv.org/abs/2302.07351 · HTML: https://mlsanity.com/paper/2302.07351/

## Key facts

- Cited by 69 papers in the map (data through 14 Oct 2026).
- Placed in Safe offline RL, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 50 of its 67 references that are in the map.
- First posted on arXiv 14 Feb 2023.

## Placed in

- Offline RL and imitation learning from data / [Safe offline RL](https://mlsanity.com/offlinerl/safe-offline-rl/)

## Builds on (most cited first, 30 of 50)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems](https://mlsanity.com/paper/2005.01643/) (2020)
- [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)
- [Off-Policy Deep Reinforcement Learning without Exploration](https://mlsanity.com/paper/1812.02900/) (2018)
- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [A Minimalist Approach to Offline Reinforcement Learning](https://mlsanity.com/paper/2106.06860/) (2021)
- [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)
- [AWAC: Accelerating Online Reinforcement Learning with Offline Datasets](https://mlsanity.com/paper/2006.09359/) (2020)
- [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://mlsanity.com/paper/2106.02039/) (2021)
- [Reward Constrained Policy Optimization](https://mlsanity.com/paper/1805.11074/) (2018)
- [Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning](https://mlsanity.com/paper/2108.06266/) (2021)
- [Risk-Constrained Reinforcement Learning with Percentile Risk Criteria](https://mlsanity.com/paper/1512.01629/) (2015)
- [Responsive Safety in Reinforcement Learning by PID Lagrangian Methods](https://mlsanity.com/paper/2007.03964/) (2020)
- [Critic Regularized Regression](https://mlsanity.com/paper/2006.15134/) (2020)
- [DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections](https://mlsanity.com/paper/1906.04733/) (2019)
- [Safe Exploration in Continuous Action Spaces](https://mlsanity.com/paper/1801.08757/) (2018)
- [Batch Policy Learning under Constraints](https://mlsanity.com/paper/1903.08738/) (2019)
- [Offline Reinforcement Learning with Fisher Divergence Critic Regularization](https://mlsanity.com/paper/2103.08050/) (2021)
- [A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems](https://mlsanity.com/paper/2203.01387/) (2022)
- [Projection-Based Constrained Policy Optimization](https://mlsanity.com/paper/2010.03152/) (2020)
- [Online Decision Transformer](https://mlsanity.com/paper/2202.05607/) (2022)
- [AlgaeDICE: Policy Gradient from Arbitrary Experience](https://mlsanity.com/paper/1912.02074/) (2019)
- [A Review of Safe Reinforcement Learning: Methods, Theory and Applications](https://mlsanity.com/paper/2205.10330/) (2022)
- [Maximum Entropy RL (Provably) Solves Some Robust RL Problems](https://mlsanity.com/paper/2103.06257/) (2021)

## Built on by (most cited first, 30 of 69)

- [Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods](https://mlsanity.com/paper/2404.00282/) (2024)
- [Diffusion Models for Reinforcement Learning: A Survey](https://mlsanity.com/paper/2311.01223/) (2023)
- [SafeDiffuser: Safe Planning with Diffusion Probabilistic Models](https://mlsanity.com/paper/2306.00148/) (2023)
- [Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model](https://mlsanity.com/paper/2401.10700/) (2024)
- [Datasets and Benchmarks for Offline Safe Reinforcement Learning](https://mlsanity.com/paper/2306.09303/) (2023)
- [TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models](https://mlsanity.com/paper/2310.05905/) (2023)
- [A Survey of Constraint Formulations in Safe Reinforcement Learning](https://mlsanity.com/paper/2402.02025/) (2024)
- [Safe Offline Reinforcement Learning with Real-Time Budget Constraints](https://mlsanity.com/paper/2306.00603/) (2023)
- [Generative Auto-Bidding with Value-Guided Explorations](https://mlsanity.com/paper/2504.14587/) (2025)
- [Survival Instinct in Offline Reinforcement Learning](https://mlsanity.com/paper/2306.03286/) (2023)
- [Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions](https://mlsanity.com/paper/2402.13777/) (2024)
- [Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning](https://mlsanity.com/paper/2310.03718/) (2023)
- [Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving](https://mlsanity.com/paper/2311.10747/) (2023)
- [Decision Mamba: A Multi-Grained State Space Model with Self-Evolution Regularization for Offline RL](https://mlsanity.com/paper/2406.05427/) (2024)
- [Off-Policy Primal-Dual Safe Reinforcement Learning](https://mlsanity.com/paper/2401.14758/) (2024)
- [Skill Expansion and Composition in Parameter Space](https://mlsanity.com/paper/2502.05932/) (2025)
- [A Review of Safe Reinforcement Learning Methods for Modern Power Systems](https://mlsanity.com/paper/2407.00304/) (2024)
- [Gradient Shaping for Multi-Constraint Safe Reinforcement Learning](https://mlsanity.com/paper/2312.15127/) (2023)
- [Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning](https://mlsanity.com/paper/2402.17217/) (2024)
- [OASIS: Conditional Distribution Shaping for Offline Safe Reinforcement Learning](https://mlsanity.com/paper/2407.14653/) (2024)
- [A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning](https://mlsanity.com/paper/2306.07818/) (2023)
- [Latent Safety-Constrained Policy Approach for Safe Offline Reinforcement Learning](https://mlsanity.com/paper/2412.08794/) (2024)
- [C-MCTS: Safe Planning with Monte Carlo Tree Search](https://mlsanity.com/paper/2305.16209/) (2023)
- [An Offline Adaptation Framework for Constrained Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2409.09958/) (2024)
- [Generative Auto-Bidding in Large-Scale Competitive Auctions via Diffusion Completer-Aligner](https://mlsanity.com/paper/2509.03348/) (2025)
- [V-OCBF: Learning Safety Filters from Offline Data via Value-Guided Offline Control Barrier Functions](https://mlsanity.com/paper/2512.10822/) (2025)
- [Adversarially Trained Weighted Actor-Critic for Safe Offline Reinforcement Learning](https://mlsanity.com/paper/2401.00629/) (2024)
- [Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcare](https://mlsanity.com/paper/2410.07525/) (2024)
- [Temporal Logic-Based Multi-Vehicle Backdoor Attacks against Offline RL Agents in End-to-end Autonomous Driving](https://mlsanity.com/paper/2509.16950/) (2025)
- [Safe Flow Q-Learning: Offline Safe Reinforcement Learning with Reachability-Based Flow Policies](https://mlsanity.com/paper/2603.15136/) (2026)

## Abstract (arXiv)

Safe reinforcement learning (RL) trains a constraint satisfaction policy by interacting with the environment. We aim to tackle a more challenging problem: learning a safe policy from an offline dataset. We study the offline safe RL problem from a novel multi-objective optimization perspective and propose the ε-reducible concept to characterize problem difficulties. The inherent trade-offs between safety and task performance inspire us to propose the constrained decision transformer (CDT) approach, which can dynamically adjust the trade-offs during deployment. Extensive experiments show the advantages of the proposed method in learning an adaptive, safe, robust, and high-reward policy. CDT outperforms its variants and strong offline safe RL baselines by a large margin with the same hyperparameters across all tasks, while keeping the zero-shot adaptation capability to different constraint thresholds, making our approach more suitable for real-world RL under constraints. The code is available at https://github.com/liuzuxin/OSRL.