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Poster
Large-Scale Multi-Agent Deep FBSDEs
Tianrong Chen · Ziyi Wang · Ioannis Exarchos · Evangelos Theodorou

Wed Jul 21 09:00 AM -- 11:00 AM (PDT) @ None #None

In this paper we present a scalable deep learning framework for finding Markovian Nash Equilibria in multi-agent stochastic games using fictitious play. The motivation is inspired by theoretical analysis of Forward Backward Stochastic Differential Equations and their implementation in a deep learning setting, which is the source of our algorithm's sample efficiency improvement. By taking advantage of the permutation-invariant property of agents in symmetric games, the scalability and performance is further enhanced significantly. We showcase superior performance of our framework over the state-of-the-art deep fictitious play algorithm on an inter-bank lending/borrowing problem in terms of multiple metrics. More importantly, our approach scales up to 3000 agents in simulation, a scale which, to the best of our knowledge, represents a new state-of-the-art. We also demonstrate the applicability of our framework in robotics on a belief space autonomous racing problem.

Author Information

Tianrong Chen (Georgia Institute of Technology)
Ziyi Wang (Georgia Institute of Technology)
Ioannis Exarchos (Stanford University)
Evangelos Theodorou (Georgia Tech)

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