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Starcraft II

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평가표: 3
보존된 평가 기록: 3

Starcraft II is a RTS game; the task is to train an agent to play the game.

<span style="color:grey; opacity: 0.6">( Image credit: The StarCraft Multi-Agent Challenge )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/marmotlab/fcmnet

공식 코드

FCMNet: Full Communication Memory Net for Team-Level Cooperation in Multi-Agent Systems

https://github.com/michalop/startrain

공식 코드

Applying supervised and reinforcement learning methods to create neural-network-based agents for playing StarCraft II

https://github.com/ml-jku/helm

공식 코드

Semantic HELM: A Human-Readable Memory for Reinforcement Learning

https://github.com/mttga/pymarl_transformers

공식 코드

TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems

https://github.com/mzho7212/LICA

공식 코드

Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning

https://github.com/navid-naderi/GraphMIX

공식 코드

Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning

https://github.com/nkai-decision-team/llm-pysc2

공식 코드

LLM-PySC2: Starcraft II learning environment for Large Language Models

https://github.com/nudt-bi-marl/pmat

공식 코드

PMAT: Optimizing Action Generation Order in Multi-Agent Reinforcement Learning

https://github.com/oxwhirl/pymarl

공식 코드

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/oxwhirl/smac

공식 코드

The StarCraft Multi-Agent Challenge

https://github.com/prashanm/StarCraft-II-causal-explanations

공식 코드

Explainable Reinforcement Learning Through a Causal Lens

https://github.com/puyuan1996/MARL

공식 코드

Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning

https://github.com/qb3/discrating

공식 코드

On the Limitations of Elo: Real-World Games, are Transitive, not Additive

https://github.com/ramsayxiaoshao/SwarmBrain-Embodied-agent-for-real-time-strategy-game-StarCraft-II-via-large-language-models

공식 코드

SwarmBrain: Embodied agent for real-time strategy game StarCraft II via large language models

https://github.com/reinholdm/offline-pre-trained-multi-agent-decision-transformer

공식 코드

Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks

https://github.com/ringbdstack/sr-marl

공식 코드

Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information Principles

https://github.com/saizhang0218/VBC

공식 코드

Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control

https://github.com/schroederdewitt/mackrl

공식 코드

Multi-Agent Common Knowledge Reinforcement Learning

https://github.com/schroederdewitt/multiagent_mujoco

공식 코드

FACMAC: Factored Multi-Agent Centralised Policy Gradients

https://github.com/sisl/DICG

공식 코드

Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning

https://github.com/somnathhazra/uncertainties_marl

공식 코드

Tackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination Dynamics

https://github.com/sri-aic/sc2-feature-extractor

공식 코드

A Framework for Understanding and Visualizing Strategies of RL Agents

https://github.com/srsohn/msgi

공식 코드

Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies

https://github.com/tartrl/tikick

공식 코드

TiKick: Towards Playing Multi-agent Football Full Games from Single-agent Demonstrations

https://github.com/tencent-ailab/tleague_projpage

공식 코드

TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement Learning

https://github.com/tonghanwang/casec-maco-benchmark

공식 코드

Context-Aware Sparse Deep Coordination Graphs

https://github.com/uoe-agents/pressureplate

공식 코드

Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization

https://github.com/uoe-agents/smaclite

공식 코드

SMAClite: A Lightweight Environment for Multi-Agent Reinforcement Learning

https://github.com/vwxyzjn/gym-microrts

공식 코드

Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning

https://github.com/vwxyzjn/gym-microrts-paper

공식 코드

Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning

https://github.com/wendelinboehmer/dcg

공식 코드

Deep Coordination Graphs

https://github.com/wjh720/QPLEX

공식 코드

QPLEX: Duplex Dueling Multi-Agent Q-Learning

https://github.com/wudiymy/dqmix

공식 코드

MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement Learning

https://github.com/wuhuikai/MSC

공식 코드

MSC: A Dataset for Macro-Management in StarCraft II

https://github.com/yiqinyang/icq

공식 코드

Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning

https://github.com/youpengzhao/crashed_agent

공식 코드

Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents

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