Multi-Agent Hierarchical Reinforcement Learning for Humanoid Navigation
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
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