Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has led to a dramatic increase in the number of applications and methods. Recent works have explored learning beyond single-agent scenarios and have considered multiagent learning (MAL) scenarios. Initial results report successes in complex multiagent domains, although there are several challenges to be addressed. The primary goal of this article is to provide a clear overview of current multiagent deep reinforcement learning (MDRL) literature. Additionally, we complement the overview with a broader analysis: (i) we revisit previous key components, originally presented in MAL and RL, and highlight how they have been adapted to multiagent deep reinforcement learning settings. (ii) We provide general guidelines to new practitioners in the area: describing lessons learned from MDRL works, pointing to recent benchmarks, and outlining open avenues of research. (iii) We take a more critical tone raising practical challenges of MDRL (e.g., implementation and computational demands). We expect this article will help unify and motivate future research to take advantage of the abundant literature that exists (e.g., RL and MAL) in a joint effort to promote fruitful research in the multiagent community.

A survey and critique of multiagent deep reinforcement learning

The advances in reinforcement learning have recorded sublime success in various domains. Although the multi-agent domain has been overshadowed by its single-agent counterpart during this progress, multi-agent reinforcement learning gains rapid traction, and the latest accomplishments address problems with real-world complexity. This article provides an overview of the current developments in the field of multi-agent deep reinforcement learning. We focus primarily on literature from recent years that combines deep reinforcement learning methods with a multi-agent scenario. To survey the works that constitute the contemporary landscape, the main contents are divided into three parts. First, we analyze the structure of training schemes that are applied to train multiple agents. Second, we consider the emergent patterns of agent behavior in cooperative, competitive and mixed scenarios. Third, we systematically enumerate challenges that exclusively arise in the multi-agent domain and review methods that are leveraged to cope with these challenges. To conclude this survey, we discuss advances, identify trends, and outline possible directions for future work in this research area.

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Multi-agent deep reinforcement learning: a survey

Energy systems undergo major transitions to facilitate the large-scale penetration of renewable energy technologies and improve efficiencies, leading to the integration of many sectors into the energy system domain. As the complexities in this domain increase, it becomes challenging to control energy flows using existing techniques based on physical models. Moreover, although data-driven models, such as reinforcement learning (RL), have gained considerable attention in many fields, a direct shift into RL is not feasible in the energy domain irrespective of the ongoing complexities. To this end, a top-down approach is used to understand this behavior by reviewing the current state of the art. We classified RL papers in the literature into seven categories based on their area of application. Subsequently, publications under each category were further examined relative to problem diversity, RL technique employed, performance improvement (compared with other white and gray box models), verification, and reproducibility; many of the articles reported a 10–20% performance improvement with the use of RL. In most studies, however, deep learning techniques and state-of-the-art actor-critic methods (e.g., twin delayed deep deterministic policy gradient and soft actor-critic) were not applied. This has remarkably hindered performance improvements and problems related to complex energy flows have not been considered. Approximately half of the publications reported the use of Q-learning. Furthermore, despite the availability of historical data in the energy system domain, batch RL algorithms have not been exploited. Emerging multi-agent RL applications may be considered as a positive development that can enable the management of complex interactions among multiple parties. Most studies lack proper benchmarking compared to model-based approaches or gray-box models, and a majority cover energy dispatch problems and building energy management. Although RL can adequately solve problems that are considerably integrated in several sectors, only a limited number of publications have discussed its broad application. The present study clearly demonstrates that even without the full utilization of RL capacity, this technique has a considerable potential in resolving the continuously increasing complexity within the energy system domain.

Applications of reinforcement learning in energy systems

Is multiagent deep reinforcement learning the answer or the question? A brief survey

Following the remarkable success of the AlphaGO series, 2019 was a booming year that witnessed significant advances in multi-agent reinforcement learning (MARL) techniques. MARL corresponds to the learning problem in a multi-agent system in which multiple agents learn simultaneously. It is an interdisciplinary domain with a long history that includes game theory, machine learning, stochastic control, psychology, and optimisation. Although MARL has achieved considerable empirical success in solving real-world games, there is a lack of a self-contained overview in the literature that elaborates the game theoretical foundations of modern MARL methods and summarises the recent advances. In fact, the majority of existing surveys are outdated and do not fully cover the recent developments since 2010. In this work, we provide a monograph on MARL that covers both the fundamentals and the latest developments in the research frontier. The goal of our monograph is to provide a self-contained assessment of the current state-of-the-art MARL techniques from a game theoretical perspective. We expect this work to serve as a stepping stone for both new researchers who are about to enter this fast-growing domain and existing domain experts who want to obtain a panoramic view and identify new directions based on recent advances.

An Overview of Multi-Agent Reinforcement Learning from Game Theoretical Perspective

Recent developments in deep reinforcement learning are concerned with creating decision-making agents which can perform well in various complex domains. A particular approach which has received increasing attention is multi-agent reinforcement learning, in which multiple agents learn concurrently to coordinate their actions. In such multi-agent environments, additional learning problems arise due to the continually changing decision-making policies of agents. This paper surveys recent works that address the non-stationarity problem in multi-agent deep reinforcement learning. The surveyed methods range from modifications in the training procedure, such as centralized training, to learning representations of the opponent's policy, meta-learning, communication, and decentralized learning. The survey concludes with a list of open problems and possible lines of future research.

Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning.

Exploration in multi-agent reinforcement learning is a challenging problem, especially in environments with sparse rewards. We propose a general method for efficient exploration by sharing experience amongst agents. Our proposed algorithm, called Shared Experience Actor-Critic (SEAC), applies experience sharing in an actor-critic framework. We evaluate SEAC in a collection of sparse-reward multi-agent environments and find that it consistently outperforms two baselines and two state-of-the-art algorithms by learning in fewer steps and converging to higher returns. In some harder environments, experience sharing makes the difference between learning to solve the task and not learning at all.

Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning

Ad hoc teamwork is the challenging problem of designing an autonomous agent which can adapt quickly to collaborate with previously unknown teammates. Prior work in this area has focused on closed teams in which the number of agents is fixed. In this work, we consider open teams by allowing agents of varying types to enter and leave the team without prior notification. Our proposed solution builds on graph neural networks to learn scalable agent models and value decompositions under varying team sizes, which can be jointly trained with a reinforcement learning agent using discounted returns objectives. We demonstrate empirically that our approach results in agent policies which can robustly adapt to dynamic team composition, and is able to effectively generalize to larger teams than were seen during training.

Open Ad Hoc Teamwork using Graph-based Policy Learning

Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used evaluation tasks and criteria, making comparisons between approaches difficult. In this work, we evaluate and compare three different classes of MARL algorithms (independent learners, centralised training with decentralised execution, and value decomposition) in a diverse range of multi-agent learning tasks. Our results show that (1) algorithm performance depends strongly on environment properties and no algorithm learns efficiently across all learning tasks; (2) independent learners often achieve equal or better performance than more complex algorithms; (3) tested algorithms struggle to solve multi-agent tasks with sparse rewards. We report detailed empirical data, including a reliability analysis, and provide insights into the limitations of the tested algorithms.

Comparative Evaluation of Multi-Agent Deep Reinforcement Learning Algorithms.

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Filippos Christianos

Papers

Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning.

Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning

Open Ad Hoc Teamwork using Graph-based Policy Learning

Comparative Evaluation of Multi-Agent Deep Reinforcement Learning Algorithms.

Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning