mlsanityData to 14 Oct 2026

Target Return Optimizer for Multi-Game Decision Transformer

arXiv 2503.02311 ↗First posted 4 Mar 2025cs.LG cs.AI cs.ROcited by 0

On the map: Offline RL and imitation learning from data → Decision transformers and sequence modeling

Abstract (arXiv)

Achieving autonomous agents with robust generalization capabilities across diverse games and tasks remains one of the ultimate goals in AI research. Recent advancements in transformer-based offline reinforcement learning, exemplified by the MultiGame Decision Transformer [Lee et al., 2022], have shown remarkable performance across various games or tasks. However, these approaches depend heavily on human expertise, presenting substantial challenges for practical deployment, particularly in scenarios with limited prior game-specific knowledge. In this paper, we propose an algorithm called Multi-Game Target Return Optimizer (MTRO) to autonomously determine game-specific target returns within the Multi-Game Decision Transformer framework using solely offline datasets. MTRO addresses the existing limitations by automating the target return configuration process, leveraging environmental reward information extracted from offline datasets. Notably, MTRO does not require additional training, enabling seamless integration into existing Multi-Game Decision Transformer architectures. Our experimental evaluations on Atari games demonstrate that MTRO enhances the performance of RL policies across a wide array of games, underscoring its potential to advance the field of autonomous agent development.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Decision transformers and sequence modeling, one of 13 directions in Offline RL and imitation learning from data. Builds on 28 of its 35 references that are in the map. First posted on arXiv 4 Mar 2025.

Builds on

28 of 35 references.

Closest ancestors · 12

Same direction, newest first

  1. 2024HarmoDT: Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning28 May
  2. 2024Return-Aligned Decision Transformer6 Feb
  3. 2023Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making4 Oct
  4. 2023ACT: Empowering Decision Transformer with Dynamic Programming via Advantage Conditioning12 Sep
  5. 2023Hyper-Decision Transformer for Efficient Online Policy Adaptation17 Apr
  6. 2022Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL8 Sep
  7. 2022Prompting Decision Transformer for Few-Shot Policy Generalization27 Jun
  8. 2022Multi-Game Decision Transformers30 May
  9. 2022Online Decision Transformer11 Feb
  10. 2021Generalized Decision Transformer for Offline Hindsight Information Matching19 Nov
  11. 2021Offline Reinforcement Learning as One Big Sequence Modeling Problem3 Jun
  12. 2021Decision Transformer: Reinforcement Learning via Sequence Modeling2 Jun

Other references · 16

  1. 2017Attention Is All You Needcited by 60,361
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2017Proximal Policy Optimization Algorithmscited by 15,215
  4. 2020Scaling Laws for Neural Language Modelscited by 6,586
  5. 2022Training Compute-Optimal Large Language Modelscited by 3,019
  6. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  7. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,809
  8. 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 807
  9. 2018Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Researchcited by 260
  10. 2021Reinforcement learning based recommender systems: A surveycited by 188
  11. 2023Constrained Decision Transformer for Offline Safe Reinforcement Learningcited by 69
  12. 2023Elastic Decision Transformercited by 67
4 more
  1. 2018Generative Adversarial User Model for Reinforcement Learning Based Recommendation Systemcited by 64
  2. 2022Learning Generalizable Dexterous Manipulation from Human Grasp Affordancecited by 51
  3. 2021Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Taskscited by 32
  4. 2021StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement Learningcited by 27

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  6. 2024Preference-Based Multi-Agent Reinforcement Learning: Data Coverage and Algorithmic Techniques
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