Multi-agent learning via gradient ascent activity-based credit assignment

We consider the situation in which cooperating agents learn to achieve a common goal based solely on a global return that results from all agents’ behavior. The method proposed is based on taking into account the agents’ activity , which can be any additional information to help solving multi-agent decentralized learning problems. We propose a gradient ascent algorithm and assess its performance on synthetic data.