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Many real-world tasks on practical control systems involve the learning and decision-making of multiple agents, under limited communications and observations. In this paper, we study the problem of networked multi-agent reinforcement learning (MARL), where multiple agents perform reinforcement learning in a common environment, and are able to exchange information via a possibly time-varying communication network. In particular, we focus on a collaborative MARL setting where each agent has individual reward functions, and the objective of all the agents is to maximize the network-wide averaged long-term return. To this end, we propose a fully decentralized actor-critic algorithm that only relies on neighbor-to-neighbor communications among agents. To promote the use of the algorithm on practical control systems, we focus on the setting with continuous state and action spaces, and adopt the newly …