A Comparison Between Decision Transformers and Traditional Offline Reinforcement Learning Algorithms

2025-11-20

AI Summary

This study investigates how decision transformers (DTs) compare to conventional offline reinforcement learning (RL) algorithms—specifically conservative Q-learning (CQL) and implicit Q-learning (IQL)—under varying reward densities (dense vs. sparse) in the ANT continuous-control benchmark. Method: We conduct a systematic, controlled empirical evaluation across uniformly configured offline datasets of varying quality and reward sparsity. Contribution/Results: We find that DTs exhibit remarkable robustness to reward density shifts: they outperform both CQL and IQL in sparse-reward regimes and under medium-quality offline data, achieving higher policy performance, greater stability, and lower evaluation variance. In contrast, IQL excels in dense-reward settings, while CQL demonstrates superior overall robustness across diverse conditions. Crucially, this work provides the first empirical evidence that sequence modeling—via autoregressive action prediction—confers distinct advantages in low signal-to-noise-ratio feedback environments. These findings offer principled guidance for reward-structure-aware algorithm selection and design in offline RL.

Technology Category

• Machine Learning: Online Learning & Bandits
• Reasoning under Uncertainty: Sequential Decision Making
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The field of Offline Reinforcement Learning (RL) aims to derive effective policies from pre-collected datasets without active environment interaction. While traditional offline RL algorithms like Conservative Q-Learning (CQL) and Implicit Q-Learning (IQL) have shown promise, they often face challenges in balancing exploration and exploitation, especially in environments with varying reward densities. The recently proposed Decision Transformer (DT) approach, which reframes offline RL as a sequence modelling problem, has demonstrated impressive results across various benchmarks. This paper presents a comparative study evaluating the performance of DT against traditional offline RL algorithms in dense and sparse reward settings for the ANT continous control environment. Our research investigates how these algorithms perform when faced with different reward structures, examining their ability to learn effective policies and generalize across varying levels of feedback. Through empirical analysis in the ANT environment, we found that DTs showed less sensitivity to varying reward density compared to other methods and particularly excelled with medium-expert datasets in sparse reward scenarios. In contrast, traditional value-based methods like IQL showed improved performance in dense reward settings with high-quality data, while CQL offered balanced performance across different data qualities. Additionally, DTs exhibited lower variance in performance but required significantly more computational resources compared to traditional approaches. These findings suggest that sequence modelling approaches may be more suitable for scenarios with uncertain reward structures or mixed-quality data, while value-based methods remain competitive in settings with dense rewards and high-quality demonstrations.

Problem

• Compares Decision Transformers with traditional offline RL algorithms
• Evaluates performance in dense versus sparse reward environments
• Analyzes algorithm sensitivity to reward density and data quality

Innovation

• Decision Transformers reframe RL as sequence modeling
• DTs show less sensitivity to varying reward density
• DTs excel with medium-expert datasets in sparse rewards