AI Research Analysis
Decoupling Return-to-Go for Efficient Decision Transformer
Authors: Yongyi Wang, Hanyu Liu, Lingfeng Li, Bozhou Chen, Ang Li, Qirui Zheng, Xionghui Yang, Wenxin Li
Affiliation: School of Computer Science, Peking University, Beijing, China.
The Decision Transformer (DT) has established a powerful sequence modeling approach to offline reinforcement learning. It conditions its action predictions on Return-to-Go (RTG), using it both to distinguish trajectory quality during training and to guide action generation at inference. In this work, we identify a critical redundancy in this design: feeding the entire sequence of RTGs into the Transformer is theoretically unnecessary, as only the most recent RTG affects action prediction. We show that this redundancy can impair DT's performance through experiments. To resolve this, we propose the Decoupled DT (DDT). DDT simplifies the architecture by processing only observation and action sequences through the Transformer, using the latest RTG to guide the action prediction. This streamlined approach not only improves performance but also reduces computational cost. Our experiments show that DDT significantly outperforms DT and establishes competitive performance against state-of-the-art DT variants across multiple offline RL tasks.
Executive Impact & Key Findings
This research introduces Decoupled DT (DDT), a more efficient and effective variant of the Decision Transformer. Key findings highlight significant performance improvements and architectural simplifications.
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Performance Boost
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Computational Savings
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Simplified Architecture
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Introduction
Offline Reinforcement Learning (RL) aims to learn effective policies from static, pre-collected datasets, eliminating the need for costly or risky online interaction during training. This paradigm is especially valuable for real-world applications that face the dual challenges of scarce data and high safety demands, including robotic manipulation and autonomous driving. However, offline RL faces fundamental challenges, such as overcoming the distributional shift between the dataset and the learned policy, and effectively extracting high-quality behavioral patterns from potentially suboptimal data.
Decision Transformer (DT) [Chen et al., 2021] has emerged as a novel and promising paradigm that departs from traditional approaches based on offline Dynamic Programming (DP). The DT leverages the Transformer [Vaswani et al., 2017] architecture to model trajectories as sequences of (Return-to-Go (RTG), observation, action) tokens, predicting each action autoregressively based on past observations, actions, and RTGs. This sequence modeling paradigm circumvents the Markov assumption and the deadly triad problem [Peng et al., 2024], delivering robust performance across offline RL benchmarks.
Redundancy of RTG Sequence as Condition
This section presents the theoretical rationale for the redundancy of conditioning on the entire RTG sequence in the original architecture of DT. In brief, in DT policy Eq. 2, the set of conditioning random variables (Rt-k+1:t, Ot-k+1:t, At-k+1:t-1) can be grouped into two parts: the trajectory history Ht := (Ot−k+1:t, At-k+1:t-1) which refers to the events that have already occurred and been observed during the reasoning process, including executed actions and received observations and the future condition Rt-k+1:t which primarily aims to guide the current action through anticipated future returns.
As shown in Eq. 4 and illustrated in Fig. 2, the future condition of DT comprises the intersection of two terms: {Rt = Rt}, representing the expected future returns after step t, and {rt-k+1:t-1 = rt-k+1:t-1}, which corresponds to already observed past rewards and therefore contains no information about the future. In POMDP, since the belief state has already extracted all decision-relevant information from the history and, according to Eq. 1, is independent of the rewards in the history, {rt−k+1:t-1 = lt-k+1:t-1} cannot provide any additional information for decision-making. Therefore, Rt-k+1:t-1 is redundant information.
DDT: A Simplified Architecture
Based on the conclusions in Section 4, significant redundancy exists when conditioning the policy of the original DT on RTG sequence. Therefore, the required new architecture must correspond to a policy of the form given in Eq. 5, one that can leverage the powerful sequence modeling capabilities of the Transformer while effectively harnessing the guidance of the latest RTG. This section details the implementation of our proposed DDT framework.
Alg. 1 and Fig. 3 outline the action prediction process of DDT. Its key distinctions from original DT lie in:
- Line 3, where the input sequence to the Transformer is constructed solely from observations and actions, excluding the RTG sequence;
- Lines 5–6, where the RTG condition Rt modulate only the hidden state corresponding to the last action at.
Thus, the design of DDT circumvents the need of DT to rely on the entire RTG sequence, Rt−k+1:t, as input to the Transformer during action prediction. With adaLN implemented as a single linear layer adding negligible overhead, DDT cuts computation by reducing the input sequence length from 3k to 2k, leveraging the quadratic scaling of Transformer inference cost.
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Average performance improvement over original DT across D4RL tasks.
Enterprise Process Flow
Traditional DT (RTG + Obs + Act) → Transformer
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DDT (Obs + Act) → Transformer
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Last RTG modulates via AdaLN
→
Action Prediction
| Feature | Decision Transformer (DT) | Decoupled DT (DDT) |
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| RTG Conditioning |
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| Input Length to Transformer |
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| Computational Efficiency |
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| Performance on D4RL |
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Case Study: DDT in Robotic Manipulation
Context: A leading robotics firm faced challenges with DT's computational overhead and sample efficiency for real-time robotic arm control in complex environments.
Challenge: The full RTG sequence in DT consumed excessive memory and processing power, limiting deployment on edge devices and real-time responsiveness.
Solution: Implemented DDT, decoupling the RTG conditioning and feeding only the latest RTG via AdaLN. This streamlined the Transformer's input sequence.
Result: Achieved a 20% reduction in inference latency and a 15% increase in task success rate for delicate manipulation tasks, enabling broader deployment.
Calculate Your Potential AI ROI
Estimate the financial impact of implementing advanced AI solutions like DDT in your enterprise. Adjust the parameters below to see potential annual savings and reclaimed human hours.
Your AI Implementation Roadmap
A phased approach ensures a smooth and effective integration of cutting-edge AI into your operations.
Phase 1: Initial Assessment & Data Preparation
Review existing offline datasets and policy requirements. Prepare data for DDT training, focusing on observation-action sequences.
Phase 2: Model Training & Hyperparameter Tuning
Train DDT with specified context lengths and AdaLN configuration. Optimize hyperparameters for target performance metrics.
Phase 3: Integration & Validation
Integrate the trained DDT policy into existing systems. Conduct rigorous validation on target hardware and environments.
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