mlsanityData to 14 Oct 2026

SMAClite: A Lightweight Environment for Multi-Agent Reinforcement Learning

arXiv 2305.05566 ↗First posted 9 May 2023cs.LG cs.AI cs.MAcited by 6

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Abstract (arXiv)

There is a lack of standard benchmarks for Multi-Agent Reinforcement Learning (MARL) algorithms. The Starcraft Multi-Agent Challenge (SMAC) has been widely used in MARL research, but is built on top of a heavy, closed-source computer game, StarCraft II. Thus, SMAC is computationally expensive and requires knowledge and the use of proprietary tools specific to the game for any meaningful alteration or contribution to the environment. We introduce SMAClite -- a challenge based on SMAC that is both decoupled from Starcraft II and open-source, along with a framework which makes it possible to create new content for SMAClite without any special knowledge. We conduct experiments to show that SMAClite is equivalent to SMAC, by training MARL algorithms on SMAClite and reproducing SMAC results. We then show that SMAClite outperforms SMAC in both runtime speed and memory.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 15 of its 31 references that are in the map. First posted on arXiv 9 May 2023.

Builds on

15 of 31 references.

References · 15

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  3. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  4. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,103
  5. 2017Counterfactual Multi-Agent Policy Gradientscited by 751
  6. 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 708
  7. 2019The StarCraft Multi-Agent Challengecited by 597
  8. 2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 503
  9. 2017StarCraft II: A New Challenge for Reinforcement Learningcited by 274
  10. 2017Emergence of Grounded Compositional Language in Multi-Agent Populationscited by 263
  11. 2018Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Researchcited by 260
  12. 2020Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Taskscited by 212
3 more
  1. 2019The Hanabi Challenge: A New Frontier for AI Researchcited by 197
  2. 2022Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workerscited by 30
  3. 2022The StarCraft Multi-Agent Challenges+ : Learning of Multi-Stage Tasks and Environmental Factors without Precise Reward Functionscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 1; 2026: 2.

6 papers.

  1. 2023JaxMARL: Multi-Agent RL Environments and Algorithms in JAXcited by 63
  2. 2024On Stateful Value Factorization in Multi-Agent Reinforcement Learningcited by 5
  3. 2024Agent-Temporal Credit Assignment for Optimal Policy Preservation in Sparse Multi-Agent Reinforcement Learningcited by 1
  4. 2026Randomness is sometimes necessary for coordinationcited by 1
  5. 2025Redistributing Rewards Across Time and Agents for Multi-Agent Reinforcement Learningcited by 0
  6. 2026Explicit Credit Assignment through Local Rewards and Dependence Graphs in Multi-Agent Reinforcement Learningcited by 0