Data-efficient multi-agent reinforcement learning - Wong, Reuben Yuh Sheng (Nanyang Technological University, 2022)
With great success in Reinforcement Learning’s application to a suite of single-agent environments, it is natural to consider its application towards environments that mimic the real world to a greater degree. One such class of environments would be decentralised multi-agent environments, mimicking the many independent agents, each with their own goals in the real-world. The decentralisation of state information, as well as constraints imposed on the behaviour of agents by local observability make this a challenging problem domain. Thankfully, there currently exists a handful of powerful algorithms operating in the co-operative multi-agent space such as QMIX, which enforce that the joint-action value is monotonic in the per-agent values, allowing the maximisation of the joint-action value in linear time during off-policy learning.