Games work on applying deep RL algorithms to single-agent and multi-agent Researchers working with deep neural network to test recent algorithms on these More recently progress has been made on developing frameworks that enable RL applied has shown that the game is an excellent testbedįor both modelling and planning (Ontanón et al., 2013), however, most haveįocused on single-agent settings with multiple controllers, and classical Learning to play StarCraft games also has been investigated in severalĬommunities: work ranging from evolutionary algorithms to tabular Straightforward 2D state observation and simple grid-world-like action spaces. Meant to provide a more challenging set of tasks with a relatively ( 2018) propose a multi-agent environment based on the gameīomberman, encompassing a series of cooperative and adversarial tasks Relatively simple for the tasks to be tractable. This work, however, focuses on testing for emergentīehaviour, since environment dynamics and control space need to remain Gridworlds focuses on many-agents tasks, where the number of agents ranges from Implementation to further explore the tasks. ( 2017) show several mixed-cooperative MarkovĮnvironment focused on testing social dilemmas, however, they did not release an ( 2017) released a set of simple grid-world likeĮnvironments for multi-agent RL alongside an implementation of MADDPG, featuringĪ mix of competitive and cooperative tasks focused on shared communication and Multiple gridworld-like environments have also been explored. Videos of our best agents for several SMAC scenarios are available at: ˙obZ0. 2 2 2Code is available at We believe that SMAC can provide a standard benchmark environment for years to come. We also open-source a deep multi-agent RL learning framework including state-of-the-art algorithms. We offer a diverse set of challenge maps and recommendations for best practices in benchmarking and evaluations. 1 1 1Code is available at SMAC is based on the popular real-time strategy game StarCraft II and focuses on micromanagement challenges where each unit is controlled by an independent agent that must act based on local observations. In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap. As a result, most papers in this field use one-off toy problems, making it difficult to measure real progress. However, there is no comparable benchmark for cooperative multi-agent RL. Standardised environments such as the ALE and MuJoCo have allowed single-agent RL to move beyond toy domains, such as grid worlds. This is an attractive research area since such problems are relevant to a large number of real-world systems and are also more amenable to evaluation than general-sum problems. A particularly challenging class of problems in this area is partially observable, cooperative, multi-agent learning, in which teams of agents must learn to coordinate their behaviour while conditioning only on their private observations. In the last few years, deep multi-agent reinforcement learning (RL) has become a highly active area of research.
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