Jakob N. Foerster1,† Gregory Farquhar1,† Triantafyllos Afouras1 Nantas Nardelli1 Shimon Whiteson1 from oxford
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.
- 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.