Browse State-of-the-Art › SMAC+
SMAC+
58 papers with code · 16 benchmarks · 17 datasets archive 2025-07-28
Bechmarks for Efficient Exploration of Completion of Multi-stage Tasks and Usage of Environmental Factors
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
16 leaderboard tables shown for this task, 16 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 16 until expanded.
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
17 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
30 shown of 58 papers with code (126 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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7 Jun 2017 86 repositories listed Syntology ran 75 of 143 samples · 68 unverified · 99 pointer-only (licence)We explore deep reinforcement learning methods for multi-agent domains.
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11 Feb 2019 23 repositories listed Syntology ran 6 of 15 samples · 9 unverified · 13 pointer-only (licence)In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap.
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30 Mar 2018 18 repositories listed Syntology ran 9 of 11 samples · 2 unverified · 6 pointer-only (licence)At the same time, it is often possible to train the agents in a centralised fashion in a simulated or laboratory setting, where global state information is available and communication constraints are lifted.
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16 Jun 2017 10 repositories listedWe study the problem of cooperative multi-agent reinforcement learning with a single joint reward signal.
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18 Nov 2020 7 repositories listedMost recently developed approaches to cooperative multi-agent reinforcement learning in the \emph{centralized training with decentralized execution} setting involve estimating a centralized, joint value function.
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24 May 2017 7 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)COMA uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents' policies.
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16 Oct 2019 4 repositories listed Syntology ran 2 of 3 samples · 1 unverified · 2 pointer-only (licence)We specifically focus on QMIX [40], the current state-of-the-art in this domain.
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14 May 2019 4 repositories listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)We explore value-based solutions for multi-agent reinforcement learning (MARL) tasks in the centralized training with decentralized execution (CTDE) regime popularized recently.
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9 Mar 2017 4 repositories listedWe present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black-box optimization by approximating the…
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6 Feb 2021 2 repositories listed Syntology ran 2 of 2 samples · 0 unverifiedMulti-Agent Reinforcement Learning (MARL) has seen revolutionary breakthroughs with its successful application to multi-agent cooperative tasks such as computer games and robot swarms.
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3 Apr 2019 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)This paper introduces an extremely efficient CNN architecture named DFANet for semantic segmentation under resource constraints.
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20 Jun 2025 1 repository listedAdapting a single agent to a new multi-agent system brings challenges, necessitating adjustments across various tasks, environments, and interactions with unknown teammates and opponents.
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9 Jun 2025 1 repository listedInspired by the success of curriculum learning (CL) in supervised tasks, we propose a dynamic CL framework for MARL that employs an self-adaptive difficulty adjustment mechanism.
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10 May 2025 1 repository listedThe Multi-Agent RL Benchmark and Learning Environment for the Robotarium (MARBLER) is an exciting recent step in providing a standardized robotics-relevant platform for MARL, by bridging the Robotarium testbed with…
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27 Mar 2025 1 repository listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)Secondly, existing works only involve common skills and can not adaptively choose independent knowledge as task-specific skills in each task for fine-grained action execution.
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7 Mar 2025 1 repository listedWe introduce Attentive VLM Agent (AVA), a multimodal StarCraft II agent that aligns artificial agent perception with the human gameplay experience.
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7 Feb 2025 1 repository listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)Multi-Agent Reinforcement Learning (MARL) has recently emerged as a significant area of research.
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4 Feb 2025 1 repository listedSecond, we propose a novel hypernet regularizer on hypernetwork weights and biases to constrain the optimization of online global Q-network to prevent overestimation accumulation.
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23 Dec 2024 1 repository listedSMAC-HARD supports customizable opponent strategies, randomization of adversarial policies, and interfaces for MARL self-play, enabling agents to generalize to varying opponent behaviors and improve model stability.
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8 Nov 2024 1 repository listedThis paper introduces a new environment LLM-PySC2 (the Large Language Model StarCraft II Learning Environment), a platform derived from DeepMind's StarCraft II Learning Environment that serves to develop Large Language…
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21 Oct 2024 1 repository listedStarCraft Multi-Agent Challenge (SMAC) is one of the most commonly used experimental environments in multi-agent reinforcement learning (MARL), where the specific task is to control a set number of allied units to…
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3 Oct 2024 1 repository listed Syntology ran 2 of 2 samples · 0 unverifiedWith expansive state-action spaces, efficient multi-agent exploration remains a longstanding challenge in reinforcement learning.
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12 Aug 2024 1 repository listedIn multi-agent cooperative tasks, the presence of heterogeneous agents is familiar.
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22 Jun 2024 1 repository listedWe cast the dynamics learning as an auto-regressive sequence modeling problem over discrete tokens by leveraging the expressive Transformer architecture, in order to model complex local dynamics across different agents…
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20 Jun 2024 1 repository listedIn this paper, we propose an enhancement to QMIX by incorporating an additional local Q-value learning method within the maximum entropy RL framework.
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28 May 2024 1 repository listedIn multi-agent reinforcement learning (MARL), effective exploration is critical, especially in sparse reward environments.
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20 May 2024 1 repository listedExtensive experiments on the SMAC benchmark demonstrate that MAZero outperforms model-free approaches in terms of sample efficiency and provides comparable or better performance than existing model-based methods in…
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5 Mar 2024 1 repository listedAgents share Q-value network periodically during the training process.
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22 Aug 2023 1 repository listedRecently, deep multi-agent reinforcement learning (MARL) has gained significant popularity due to its success in various cooperative multi-agent tasks.
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7 Aug 2023 1 repository listedTraditional methods, like grid search and Bayesian optimization, often struggle to quickly adapt and efficiently search the loss landscape.
Syntology lines on 11 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections