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
