Cooperative multi-agent systems can be naturally used to model many real world problems, such as network packet routing or the coordination of autonomous vehicles. There is a great need for new reinforcement learning methods that can efficiently learn decentralised policies for such systems. To this end, we propose a new multi-agent actor-critic method called counterfactual multi-agent (COMA) policy gradients. COMA uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents' policies. In addition, to address the challenges of multi-agent credit assignment, it uses a counterfactual baseline that marginalises out a single agent's action, while keeping the other agents' actions fixed. COMA also uses a critic representation that allows the counterfactual baseline to be computed efficiently in a single forward pass. We evaluate COMA in the testbed of StarCraft unit micromanagement, using a decentralised variant with significant partial observability. COMA significantly improves average performance over other multi-agent actor-critic methods in this setting, and the best performing agents are competitive with state-of-the-art centralised controllers that get access to the full state. Another crucial challenge is multi-agent credit assignment (Chang et al., 2003) : in cooperative settings, joint actions typically generate only global rewards, making it difficult for each agent to † These authors contributed equally to this work.
COMA REF ) uses a centralised critic to train decentralised actors, estimating a counterfactual advantage function for each agent in order to address multi-agent credit assignment.
Counterfactual Multi-Agent Policy Gradients
ArXiv
ArXiv
Computer Science