mlsanityData to 14 Oct 2026

Multi-agent transformer-accelerated RL for satisfaction of STL specifications

arXiv 2403.15916 ↗First posted 23 Mar 2024cs.AIcited by 1

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Abstract (arXiv)

One of the main challenges in multi-agent reinforcement learning is scalability as the number of agents increases. This issue is further exacerbated if the problem considered is temporally dependent. State-of-the-art solutions today mainly follow centralized training with decentralized execution paradigm in order to handle the scalability concerns. In this paper, we propose time-dependent multi-agent transformers which can solve the temporally dependent multi-agent problem efficiently with a centralized approach via the use of transformers that proficiently handle the large input. We highlight the efficacy of this method on two problems and use tools from statistics to verify the probability that the trajectories generated under the policy satisfy the task. The experiments show that our approach has superior performance against the literature baseline algorithms in both cases.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 25 references that are in the map. First posted on arXiv 23 Mar 2024.

Builds on

14 of 25 references.

References · 14

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  3. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  4. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,546
  5. 2022Multi-Agent Reinforcement Learning is a Sequence Modeling Problemcited by 155
  6. 2021Learnable Fourier Features for Multi-Dimensional Spatial Positional Encodingcited by 93
  7. 2016Q-Learning for Robust Satisfaction of Signal Temporal Logic Specificationscited by 67
  8. 2020Cautious Reinforcement Learning with Logical Constraintscited by 45
  9. 2021Settling the Variance of Multi-Agent Policy Gradientscited by 45
  10. 2021UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformerscited by 39
  11. 2019Reinforcement Learning with Probabilistic Guarantees for Autonomous Drivingcited by 28
  12. 2020Recurrent Neural Network Controllers for Signal Temporal Logic Specifications Subject to Safety Constraintscited by 18
2 more
  1. 2021On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learningcited by 10
  2. 2022STL-Based Synthesis of Feedback Controllers Using Reinforcement Learningcited by 4

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Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024Sable: a Performant, Efficient and Scalable Sequence Model for MARLcited by 10

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  2. 2026Bridging MARL to SARL: An Order-Independent Multi-Agent Transformer via Latent Consensus
  3. 2026Generalizable Multi-Agent Planning from Signal Temporal Logic Specifications via Diffusion
  4. 2025Multi-Agent Path Finding via Offline RL and LLM Collaboration
  5. 2025Transformer-Based Multi-Agent Reinforcement Learning for Networked Systems with Long-Range Interactions
  6. 2025Scalable Multi-Agent Offline Reinforcement Learning and the Role of Information
  7. 2024Reducing Redundant Computation in Multi-Agent Coordination through Locally Centralized Execution
  8. 2025Structured Cooperative Multi-Agent Reinforcement Learning: a Bayesian Network Perspective