SMAC
38 papers with code • 11 benchmarks • 1 datasets
The StarCraft Multi-Agent Challenge (SMAC) is a benchmark that provides elements of partial observability, challenging dynamics, and high-dimensional observation spaces. SMAC is built using the StarCraft II game engine, creating a testbed for research in cooperative MARL where each game unit is an independent RL agent.
Benchmarks
These leaderboards are used to track progress in SMAC
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Libraries
Use these libraries to find SMAC models and implementations
SMAC
Latest papers with no code
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning
One of the notorious issues for Reinforcement Learning (RL) is poor sample efficiency.
Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration
Second, we introduce a heterogeneous layer for decision-making, whose parameters are specifically generated by the learned latent variables.
Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning
To address this limitation, we propose Imagine, Initialize, and Explore (IIE), a novel method that offers a promising solution for efficient multi-agent exploration in complex scenarios.
Aligning Individual and Collective Objectives in Multi-Agent Cooperation
The visualization of learning dynamics effectively demonstrates that AgA successfully achieves alignment between individual and collective objectives.
Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation
Multi-Agent Reinforcement Learning (MARL) algorithms are widely adopted in tackling complex tasks that require collaboration and competition among agents in dynamic Multi-Agent Systems (MAS).
MAIDCRL: Semi-centralized Multi-Agent Influence Dense-CNN Reinforcement Learning
Distributed decision-making in multi-agent systems presents difficult challenges for interactive behavior learning in both cooperative and competitive systems.
Poisson Process for Bayesian Optimization
BayesianOptimization(BO) is a sample-efficient black-box optimizer, and extensive methods have been proposed to build the absolute function response of the black-box function through a probabilistic surrogate model, including Tree-structured Parzen Estimator (TPE), random forest (SMAC), and Gaussian process (GP).
T2MAC: Targeted and Trusted Multi-Agent Communication through Selective Engagement and Evidence-Driven Integration
This process enables agents to collectively use evidence garnered from multiple perspectives, fostering trusted and cooperative behaviors.
Innate-Values-driven Reinforcement Learning for Cooperative Multi-Agent Systems
This paper proposes a hierarchical compound intrinsic value reinforcement learning model -- innate-values-driven reinforcement learning termed IVRL to describe the complex behaviors of multi-agent interaction in their cooperation.
How much can change in a year? Revisiting Evaluation in Multi-Agent Reinforcement Learning
Establishing sound experimental standards and rigour is important in any growing field of research.
