Constrained Decision Transformer for Offline Safe Reinforcement Learning

• URL: http://arxiv.org/abs/2302.07351v2
• Date: Wed, 21 Jun 2023 06:07:22 GMT
• Title: Constrained Decision Transformer for Offline Safe Reinforcement Learning
• Authors: Zuxin Liu, Zijian Guo, Yihang Yao, Zhepeng Cen, Wenhao Yu, Tingnan Zhang, Ding Zhao
• Abstract summary: We study the offline safe RL problem from a novel multi-objective optimization perspective. We propose the constrained decision transformer (CDT) approach, which can dynamically adjust the trade-offs during deployment.
• Score: 16.485325576173427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $\epsilon$-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.

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