  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.
