Decentralized Deep Reinforcement Learning for Network Level Traffic Signal Control

2 Jul 2020 Jin Guo

In this thesis, I propose a family of fully decentralized deep multi-agent reinforcement learning (MARL) algorithms to achieve high, real-time performance in network-level traffic signal control. In this approach, each intersection is modeled as an agent that plays a Markovian Game against the other intersection nodes in a traffic signal network modeled as an undirected graph, to approach the optimal reduction in delay... (read more)

PDF Abstract

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper