Multi-agent transformer-accelerated RL for satisfaction of STL specifications
<|reference_start|>Multi-agent transformer-accelerated RL for satisfaction of STL specifications: 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.<|reference_end|>
@article{forsberg2024multi-agent,
    title={Multi-agent transformer-accelerated RL for satisfaction of STL
  specifications},
    author={Albin Larsson Forsberg and Alexandros Nikou and Aneta Vulgarakis
  Feljan and Jana Tumova},
    journal={arXiv preprint arXiv:2403.15916},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15916},
    primaryClass={cs.AI}
}