A Communication-Efficient Multi-Agent Actor-Critic Algorithm for Distributed Reinforcement Learning

Recently, there has been increasing interest in developing distributed machine learning algorithms. Notable examples include distributed linear regression [1], multi-arm bandit [2], reinforcement learning (RL) [3], and deep learning [4]. Such algorithms have promising applications in large-scale networks, such as social platforms, online economic networks, cyber-physical systems, and Internet of Things, primarily because in such a complex network, it is impossible to collect all the information at the same point and each component of the network may not be willing to share its private information due to privacy issues. Multi-agent reinforcement learning (MARL) problems have recently received increasing attention. In general, MARL problems are investigated in settings that are either collaborative, competitive, or a mixture of the two. For collaborative MARL, the most rudimentary framework is the canonical multi-agent Markov decision process [5, 6], where the agents share a common reward function that is determined by the joint actions of all agents. Another notable framework for collaborative MARL is the team Markov game model, also with a shared reward function among agents [7, 8]. These two frameworks were then extended to the setting where agents are allowed to have heterogeneous reward functions[3,9-12], collaborating with the goal of maximizing the long-term return corresponding to the team averaged reward.