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
Better Understandings and Configurations in MaxSAT Local Search Solvers via Anytime Performance Analysis
Though numerous solvers have been proposed for the MaxSAT problem, and the benchmark environment such as MaxSAT Evaluations provides a platform for the comparison of the state-of-the-art solvers, existing assessments were usually evaluated based on the quality, e. g., fitness, of the best-found solutions obtained within a given running time budget.
PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement Learning
Agents share Q-value network periodically during the training process.
FoX: Formation-aware exploration in multi-agent reinforcement learning
hyeon1996/fox
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Recently, deep multi-agent reinforcement learning (MARL) has gained significant popularity due to its success in various cooperative multi-agent tasks.
HomOpt: A Homotopy-Based Hyperparameter Optimization Method
Traditional methods, like grid search and Bayesian optimization, often struggle to quickly adapt and efficiently search the loss landscape.
A Unified Framework for Factorizing Distributional Value Functions for Multi-Agent Reinforcement Learning
In fully cooperative multi-agent reinforcement learning (MARL) settings, environments are highly stochastic due to the partial observability of each agent and the continuously changing policies of other agents.
Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers
zzq-bot/romance
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Concretely, to avoid the ego-system overfitting to a specific attacker, we maintain a set of attackers, which is optimized to guarantee the attackers high attacking quality and behavior diversity.
SMAClite: A Lightweight Environment for Multi-Agent Reinforcement Learning
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.
Automated classification of pre-defined movement patterns: A comparison between GNSS and UWB technology
However, to date, few studies have investigated the performance of different localisation systems regarding the classification of human movement patterns in small areas.
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence
dig-beihang/ami
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To achieve maximum deviation in victim policies under complex agent-wise interactions, our unilateral attack aims to characterize and maximize the impact of the adversary on the victims.
Self-Motivated Multi-Agent Exploration
In cooperative multi-agent reinforcement learning (CMARL), it is critical for agents to achieve a balance between self-exploration and team collaboration.
