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A review of cooperative multi-agent deep reinforcement learning

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Abstract

Deep Reinforcement Learning has made significant progress in multi-agent systems in recent years. The aim of this review article is to provide an overview of recent approaches on Multi-Agent Reinforcement Learning (MARL) algorithms. Our classification of MARL approaches includes five categories for modeling and solving cooperative multi-agent reinforcement learning problems: (I) independent learners, (II) fully observable critics, (III) value function factorization, (IV) consensus, and (IV) learn to communicate. We first discuss each of these methods, their potential challenges, and how these challenges were mitigated in the relevant papers. Additionally, we make connections among different papers in each category if applicable. Next, we cover some new emerging research areas in MARL along with the relevant recent papers. In light of MARL’s recent success in real-world applications, we have dedicated a section to reviewing these applications and articles. This survey also provides a list of available environments for MARL research. Finally, the paper is concluded with proposals on possible research directions.

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Notes

  1. A policy can be deterministic or stochastic. Action at is the direct outcome of a deterministic policy, i.e., at = π(st). In stochastic policies, the outcome of the policy is the probability of choosing each of the actions, i.e., π(a|st) = Pr(at = a|st), and then an additional method is required to choose an action among them. For example, a greedy method chooses the action with the highest probability. In this paper, when we refer to an action resulted from a policy we mostly use the notation for the stochastic policy, \(a \sim \pi (.|s)\).

  2. GTD algorithm is proposed to stabilize the TD algorithm with linear function approximation in an off-policy setting.

  3. Arrow-Hurwicz is a primal-dual optimization algorithm that performs the gradient step on the Lagrangian over the primal and dual variables iteratively

  4. Semi-cooperative environments are those that each agent looks for its own goal while all agents also want to maximize a common goal.

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    Afshin Oroojlooy & Davood Hajinezhad

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Oroojlooy, A., Hajinezhad, D. A review of cooperative multi-agent deep reinforcement learning. Appl Intell 53, 13677–13722 (2023). https://doi.org/10.1007/s10489-022-04105-y

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