Pith. sign in

REVIEW 27 cited by

The StarCraft Multi-Agent Challenge

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1902.04043 v5 pith:NVVQACU7 submitted 2019-02-11 cs.LG cs.MAstat.ML

Mikayel Samvelyan , Tabish Rashid , Christian Schroeder de Witt , Gregory Farquhar , Nantas Nardelli , Tim G. J. Rudner , Chia-Man Hung , Philip H. S. Torr
show 2 more authors
Jakob Foerster Shimon Whiteson
This is my paper · ORCID
classification cs.LGcs.MAstat.ML
keywords multi-agentproblemssmacareabenchmarkchallengelearningstarcraft
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In the last few years, deep multi-agent reinforcement learning (RL) has become a highly active area of research. 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. 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. Standardised environments such as the ALE and MuJoCo have allowed single-agent RL to move beyond toy domains, such as grid worlds. However, there is no comparable benchmark for cooperative multi-agent RL. As a result, most papers in this field use one-off toy problems, making it difficult to measure real progress. In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap. 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. We offer a diverse set of challenge maps and recommendations for best practices in benchmarking and evaluations. We also open-source a deep multi-agent RL learning framework including state-of-the-art algorithms. We believe that SMAC can provide a standard benchmark environment for years to come. Videos of our best agents for several SMAC scenarios are available at: https://youtu.be/VZ7zmQ_obZ0.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 27 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DungeonBench: A Benchmark for Rules-Rich Tactical Reasoning in Dungeons & Dragons Combat

    cs.AI 2026-07 conditional novelty 7.0 of 10

    DungeonBench scores LLM tactical play on D&D combat, finding frontier policies clear ~80% of single encounters but only 40% of linked multi-encounter days.

  2. Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics

    cs.AI 2025-07 conditional novelty 7.0 of 10

    Assistax provides fast JAX-based assistive robotics environments with trainable humanoid partners, and shows current RL baselines have a coordination gap when facing unseen human preferences.

  3. TABX: A High-Throughput Sandbox Battle Simulator for Multi-Agent Reinforcement Learning

    cs.MA 2026-02 unverdicted novelty 6.0 of 10

    TABX is a JAX-based, GPU-accelerated, configurable multi-agent battle simulator that lets researchers vary units, terrain, and physics to benchmark cooperative MARL algorithms.

  4. PillagerBench: Benchmarking LLM-Based Agents in Competitive Minecraft Team Environments

    cs.AI 2025-09 conditional novelty 6.0 of 10

    A new open Minecraft benchmark for 2v2 LLM-agent competition, and a system, TactiCrafter, that beats its baselines on points and win rate.

  5. SC2Arena and StarEvolve: Benchmark and Self-Improvement Framework for LLMs in Complex Decision-Making Tasks

    cs.LG 2025-08 conditional novelty 6.0 of 10

    The authors propose SC2Arena, a full-coverage StarCraft II benchmark for LLMs, and StarEvolve, a planner-executor-verifier self-improvement framework, claiming superior strategic planning.

  6. Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning

    cs.LG 2025-07 conditional novelty 6.0 of 10

    A feudal hierarchical MARL method where lower-level policies are rewarded with the upper level's advantage function, with theoretical alignment guarantees and strong benchmark results.

  7. Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review

    cs.AI 2025-07 conditional novelty 6.0 of 10

    A survey proposing adaptability as a three-part taxonomy (learning, policy, scenario-driven) for organizing and evaluating MARL under changing conditions.

  8. StarDojo: Benchmarking Open-Ended Behaviors of Agentic Multimodal LLMs in Production-Living Simulations with Stardew Valley

    cs.AI 2025-07 conditional novelty 6.0 of 10

    StarDojo is a 1,000-task benchmark in Stardew Valley combining production and social activities, and the best tested MLLM (GPT-4.1) achieves only 12.7% success on its 100-task subset.

  9. Artificial Generals Intelligence: Mastering Generals.io with Reinforcement Learning

    cs.LG 2025-07 conditional novelty 6.0 of 10

    A PPO agent trained with behavior cloning, self-play, and reward shaping reaches a 54.82% win rate against the previous best Generals.io bot and a reported top-25 human leaderboard position.

  10. Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals

    cs.LG 2025-07 conditional novelty 6.0 of 10

    Unrewarded bottleneck transitions, called zero-incentive dynamics, cause state-of-the-art subgoal-based RL methods to fail, and learning quality degrades sharply with delay between subgoal completion and reward.

  11. Action Dependency Graphs for Globally Optimal Coordinated Reinforcement Learning

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A sparse action dependency graph derived from the coordination graph is sufficient for a locally optimal policy to be globally optimal in cooperative multi-agent RL.

  12. Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean-Field Games

    stat.ML 2025-05 conditional novelty 6.0 of 10

    Exact and sample-based trust-region policy optimization provably converge to approximate Nash equilibria in finite mean-field games with Õ(1/ε^6) sample complexity.

  13. AT-Drone: Benchmarking Adaptive Teaming in Multi-Drone Pursuit

    cs.RO 2025-02 conditional novelty 6.0 of 10

    AT-Drone is a benchmark for adaptive teaming in multi-drone pursuit, with four environments, seven algorithms, and real-world Crazyflie drone validation.

  14. Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning

    cs.MA 2025-02 conditional novelty 6.0 of 10

    Per-agent low-rank adapters on a shared backbone let multi-agent policies specialize at a fraction of the memory cost of separate networks, with competitive benchmark performance.

  15. An Extended Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Complex Fully Cooperative Tasks

    cs.LG 2025-02 conditional novelty 6.0 of 10

    Algorithms that set state of the art on SMAC and GRF often underperform standard baselines on fully cooperative benchmarks, including image-based tasks.

  16. Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents

    cs.MA 2026-07 conditional novelty 5.0 of 10

    Feeding each agent a summed map of the instructions given to the other agents makes uninstructed agents cover overlooked areas and improves object collection by roughly 8% over the baseline in the tested game.

  17. Self-Supervised Goal-Reaching Results in Multi-Agent Cooperation and Exploration

    cs.LG 2025-09 conditional novelty 5.0 of 10

    Self-supervised multi-agent goal-reaching, where each agent independently learns a contrastive critic of its own observations, achieves cooperation and exploration in sparse-reward MARL tasks where standard baselines fail.

  18. cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending

    cs.LG 2025-08 reject novelty 5.0 of 10

    cMALC-D uses an LLM to generate training contexts for multi-agent RL and a diversity-blending mechanism to avoid mode collapse, claiming improved generalization on traffic signal control.

  19. From General Relation Patterns to Task-Specific Decision-Making in Continual Multi-Agent Coordination

    cs.MA 2025-07 conditional novelty 5.0 of 10

    RPG uses a relation capturer plus a task-conditioned hypernetwork to reduce catastrophic forgetting and enable zero-shot transfer in continual multi-agent coordination.

  20. Learning Bilateral Team Formation in Cooperative Multi-Agent Reinforcement Learning

    cs.MA 2025-06 conditional novelty 5.0 of 10

    A stable-matching-based team formation method improves generalization over a greedy score-based method in cooperative multi-agent RL.

  21. Light Aircraft Game : Basic Implementation and training results analysis

    cs.LG 2025-06 reject novelty 5.0 of 10

    In the new LAG air-combat environment, HASAC scores higher than HAPPO in no-weapon coordination tasks while HAPPO scores higher in missile combat, but the results come from single runs without error bars.

  22. Single-Agent Planning in a Multi-Agent System: A Unified Framework for Type-Based Planners

    cs.MA 2025-02 conditional novelty 5.0 of 10

    A layered tree-search framework unifies type-based opponent-modelling planners, and myopic safe-agents emerge as the strongest practical choice in a large multi-agent route planning benchmark.

  23. Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs

    cs.LG 2025-05 reject novelty 4.0 of 10

    Adaptively quantizing parts of an LLM's layers to FP4 can improve win rates and trading yields in latency-sensitive agent tasks, but the reported gains come from choosing the best compression level after seeing test results.

  24. A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games

    cs.LG 2025-09 conditional novelty 3.0 of 10

    A survey of multi-agent reinforcement learning in video games, plus a proposed five-dimension, MDP-based classification for comparing game complexity.

  25. GenAI-based Multi-Agent Reinforcement Learning towards Distributed Agent Intelligence: A Generative-RL Agent Perspective

    cs.AI 2025-07 unverdicted novelty 3.0 of 10

    A position paper claiming that generative-AI agents that model and predict multi-agent dynamics will replace today's reactive MARL approaches.

  26. Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications

    cs.MA 2025-05 conditional novelty 3.0 of 10

    A narrative survey of multi-agent reinforcement learning for cyber defense, reviewing game-theoretic models, cyber gyms, and applications, concluding MARL is promising but faces scalability and simulation-to-real tran...

  27. MEAL: A Benchmark for Continual Multi-Agent Reinforcement Learning

    cs.AI 2025-06

Pith tools