Cooperative Multi-Agent Learning for Navigation via Structured State Abstraction

Understand

Cooperative multi-agent reinforcement learning (MARL) for navigation enables agents to cooperate to achieve their navigation goals.

• Using emergent communication, agents learn a communication protocol to coordinate and share information that is needed to achieve their navigation tasks.
• In emergent communication, symbols with no pre-specified usage rules are exchanged, in which the meaning and syntax emerge through training.
• Learning a navigation policy along with a communication protocol in a MARL environment is highly complex due to the huge state space to be explored.