Starcraft II
79 papers with code • 3 benchmarks • 4 datasets
Starcraft II is a RTS game; the task is to train an agent to play the game.
( Image credit: The StarCraft Multi-Agent Challenge )
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Use these libraries to find Starcraft II models and implementationsLatest papers with no code
Collaborative AI Teaming in Unknown Environments via Active Goal Deduction
With the advancements of artificial intelligence (AI), we're seeing more scenarios that require AI to work closely with other agents, whose goals and strategies might not be known beforehand.
SMAUG: A Sliding Multidimensional Task Window-Based MARL Framework for Adaptive Real-Time Subtask Recognition
Instead of making behavioral decisions directly from the exponentially expanding joint observational-action space, subtask-based multi-agent reinforcement learning (MARL) methods enable agents to learn how to tackle different subtasks.
COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations
The development of Courses of Action (COAs) in military operations is traditionally a time-consuming and intricate process.
BET: Explaining Deep Reinforcement Learning through The Error-Prone Decisions
To address this issue, we propose a novel self-interpretable structure, named Backbone Extract Tree (BET), to better explain the agent's behavior by identify the error-prone states.
StarCraftImage: A Dataset For Prototyping Spatial Reasoning Methods For Multi-Agent Environments
Spatial reasoning tasks in multi-agent environments such as event prediction, agent type identification, or missing data imputation are important for multiple applications (e. g., autonomous surveillance over sensor networks and subtasks for reinforcement learning (RL)).
DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning
In this paper, we propose a new approach that enables agents to learn whether their behaviors should be consistent with that of other agents by utilizing intrinsic rewards to learn the optimal policy for each agent.
Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play
In this paper, we propose the Minimax Exploiter, a game theoretic approach to exploiting Main Agents that leverages knowledge of its opponents, leading to significant increases in data efficiency.
MIR2: Towards Provably Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization
Existing max-min optimization techniques in robust MARL seek to enhance resilience by training agents against worst-case adversaries, but this becomes intractable as the number of agents grows, leading to exponentially increasing worst-case scenarios.
Fidelity-Induced Interpretable Policy Extraction for Reinforcement Learning
We then design a fidelity-induced mechanism by integrate a fidelity measurement into the reinforcement learning feedback.
Leveraging World Model Disentanglement in Value-Based Multi-Agent Reinforcement Learning
In this paper, we propose a novel model-based multi-agent reinforcement learning approach named Value Decomposition Framework with Disentangled World Model to address the challenge of achieving a common goal of multiple agents interacting in the same environment with reduced sample complexity.