title: Multi-Agent Hierarchical Reinforcement Learning for Humanoid Navigation
abstract: Multi-agent reinforcement learning is a particularly challenging problem. Current
  methods have made progress on cooperative and competitive environments with
  particle-based agents. Little progress has been made on solutions that could op-
  erate in the real world with interaction, dynamics, and humanoid robots. In this
  work, we make a significant step in multi-agent models on simulated humanoid
  robot  navigation  by  combining  Multi-Agent  Reinforcement  Learning  (MARL)
  with  Hierarchical  Reinforcement  Learning  (HRL).  We  build  on  top  of  founda-
  tional prior  work in  learning low-level physical  controllers for locomotion  and
  add a layer to learn decentralized policies for multi-agent goal-directed collision
  avoidance systems. A video of our results on a multi-agent pursuit environment
  can be seen here
  
authors: Glen Berseth Brandon haworth Seonghyeon Moon Mubbasir Kapadia Petros Faloutsos
hIndex: Petros Faloutsos 32