arXiv:2511.16475v1 Announce Type: new 
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.

تناقش الورقة البحثية التعلم المعزز غير المتصل (RL)، الذي يركز على اشتقاق سياسات فعالة من مجموعات بيانات تم جمعها مسبقًا دون تفاعل نشط. تواجه الخوارزميات التقليدية مثل التعلم المحافظ Q (CQL) والتعلم الضمني Q (IQL) تحديات في تحقيق التوازن بين الاستكشاف والاستغلال. لقد أظهرت طريقة Decision Transformer (DT)، التي تعيد صياغة التعلم المعزز غير المتصل كمشكلة نمذجة تسلسلية، نتائج قوية في مختلف المعايير. تقدم هذه الدراسة مقارنة بين DT والخوارزميات التقليدية في إعدادات مكافآت كثيفة ونادرة باستخدام بيئة التحكم المستمر ANT.

El artículo analiza el Aprendizaje por Refuerzo Offline (RL), que se centra en derivar políticas efectivas a partir de conjuntos de datos precoleccionados sin interacción activa. Los algoritmos tradicionales como el Conservative Q-Learning (CQL) y el Implicit Q-Learning (IQL) enfrentan desafíos para equilibrar la exploración y la explotación. El enfoque Decision Transformer (DT), que trata el RL offline como un problema de modelado de secuencias, ha mostrado un rendimiento sólido en varios benchmarks. Este estudio compara DT con algoritmos tradicionales en entornos de recompensas densas y esca…

Cet article traite de l'apprentissage par renforcement hors ligne (RL), qui vise à dériver des politiques efficaces à partir de jeux de données pré-collectés sans interaction active. Les algorithmes traditionnels comme le Conservative Q-Learning (CQL) et l'Implicit Q-Learning (IQL) rencontrent des difficultés pour équilibrer exploration et exploitation. L'approche Decision Transformer (DT), qui considère le RL hors ligne comme un problème de modélisation de séquence, a montré de solides performances dans divers benchmarks. Cette étude compare DT avec des algorithmes traditionnels dans des envi…

The paper discusses Offline Reinforcement Learning (RL), which focuses on deriving effective policies from pre-collected datasets without active interaction. Traditional algorithms like Conservative Q-Learning (CQL) and Implicit Q-Learning (IQL) face challenges in balancing exploration and exploitation. The Decision Transformer (DT) approach, which treats offline RL as a sequence modeling problem, has shown strong performance in various benchmarks. This study compares DT with traditional algorithms in dense and sparse reward settings using the ANT continuous control environment.

A Comparison Between Decision Transformers and Traditional Offline Reinforcement Learning Algorithms

arXiv:2408.01402v2 Announce Type: replace 
Abstract: Decision Transformer (DT) has emerged as a promising class of algorithms in offline reinforcement learning (RL) tasks, leveraging pre-collected datasets and Transformer's capability to model long sequences. Recent works have demonstrated that using parts of trajectories from training tasks as prompts in DT enhances its performance on unseen tasks, giving rise to Prompt-DT methods. However, collecting data from specific environments can be both costly and unsafe in many scenarios, leading to suboptimal performance and limited few-shot prompt abilities due to the data-hungry nature of Transformer-based models. Additionally, the limited datasets used in pre-training make it challenging for Prompt-DT type of methods to distinguish between various RL tasks through prompts alone. To address these challenges, we introduce the Language model-initialized Prompt Decision Transformer (LPDT) framework, which leverages pretrained language models providing rich prior knowledge for RL tasks and fine-tunes the sequence model using Low-rank Adaptation (LoRA) for meta-RL problems. We further incorporate prompt regularization to effectively differentiate between tasks based on prompt feature representations. Comprehensive empirical studies demonstrate that initializing with a pre-trained language model provides the prior knowledge and achieves a similar performance with Prompt-DT under only $10\%$ data in some MuJoCo control tasks. We also provide a thorough ablation study to validate the effectiveness of each component, including sequence modeling, language models, prompt regularizations, and prompt strategies.

يمثل تقديم إطار عمل Language model-initialized Prompt Decision Transformer (LPDT) تقدمًا كبيرًا في التعلم المعزز غير المتصل (RL) من خلال تحسين قدرة النماذج على استخدام المحفزات القليلة. يستخدم هذا الإطار نماذج اللغة المدربة مسبقًا لتحسين الأداء في المهام غير المرئية، مما يعالج التحديات المتعلقة بجمع البيانات والقيود المفروضة على طرق Prompt-DT التقليدية.

La introducción del marco Language model-initialized Prompt Decision Transformer (LPDT) marca un avance significativo en el aprendizaje por refuerzo offline (RL) al mejorar la capacidad de prompt de pocos disparos de los Decision Transformers. Este marco utiliza modelos de lenguaje preentrenados para mejorar el rendimiento en tareas no vistas, abordando desafíos relacionados con la recopilación de datos y las limitaciones de los métodos tradicionales de Prompt-DT.

L'introduction du cadre Language model-initialized Prompt Decision Transformer (LPDT) représente une avancée significative dans l'apprentissage par renforcement hors ligne (RL) en améliorant la capacité de prompt à quelques exemples des Decision Transformers. Ce cadre utilise des modèles de langage pré-entraînés pour améliorer les performances sur des tâches non vues, en abordant les défis liés à la collecte de données et aux limitations des méthodes Prompt-DT traditionnelles.

The introduction of the Language model-initialized Prompt Decision Transformer (LPDT) framework marks a significant advancement in offline reinforcement learning (RL) by enhancing the few-shot prompt ability of Decision Transformers. This framework utilizes pre-trained language models to improve performance on unseen tasks, addressing challenges related to data collection and the limitations of traditional Prompt-DT methods.

A Comparison Between Decision Transformers and Traditional Offline Reinforcement Learning Algorithms

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