Multi-agent transformer-accelerated RL for satisfaction of STL specifications
This work addresses scalability issues in multi-agent systems for tasks with temporal dependencies, representing an incremental improvement over existing centralized training methods.
The paper tackles the scalability challenge in multi-agent reinforcement learning for temporally dependent problems by proposing time-dependent multi-agent transformers, achieving superior performance against baseline algorithms in experiments.
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.