Promoting Coordination through Policy Regularization in Multi-Agent Reinforcement Learning

Barde, Paul, Roy, Julien, Harvey, Félix G., Nowrouzezahrai, Derek, Pal, Christopher Machine Learning 

A central challenge in multi-agent reinforcement learning is the induction of coordination between agents of a team. In this work, we investigate how to promote inter-agent coordination and discuss two possible avenues based respectively on inter-agent modelling and guided synchronized sub-policies. We test each approach in four challenging continuous control tasks with sparse rewards and compare them against three variants of MADDPG, a state-of-the-art multi-agent reinforcement learning algorithm. To ensure a fair comparison, we rely on a thorough hyper-parameter selection and training methodology that allows a fixed hyper-parameter search budget for each algorithm and environment. We consequently assess both the hyper-parameter sensitivity, sample-efficiency and asymptotic performance of each learning method. Our experiments show that our proposed algorithms are more robust to the hyper-parameter choice and reliably lead to strong results.