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Real-Time Strategy Games

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Real-Time Strategy (RTS) tasks involve training an agent to play video games with continuous gameplay and high-level macro-strategic goals such as map control, economic superiority and more.

<span style="color:grey; opacity: 0.6">( Image credit: Multi-platform Version of StarCraft: Brood War in a Docker Container )</span>

코드

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

https://github.com/lxxxxr/ices

공식 코드

Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning

https://github.com/lxxxxr/kaleidoscope

공식 코드

Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning

https://github.com/marlbenchmark/on-policy

공식 코드

The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games

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/morning9393/optimal-baseline-for-multi-agent-policy-gradients

공식 코드

Settling the Variance of Multi-Agent Policy Gradients

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/nanxintin/StarCraft-AI

공식 코드

StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer 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

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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/opendilab/ace

공식 코드

ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency

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/oxwhirl/smacv2

공식 코드

SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning

https://github.com/oxwhirl/wqmix

공식 코드

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

https://github.com/paleroy/competpymarl

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Value-based CTDE Methods in Symmetric Two-team Markov Game: from Cooperation to Team Competition

https://github.com/paleroy/competsmac

공식 코드

Value-based CTDE Methods in Symmetric Two-team Markov Game: from Cooperation to Team Competition

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

공식 코드

Explainable Reinforcement Learning Through a Causal Lens

https://github.com/puyuan1996/MARL

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

https://github.com/qb3/discrating

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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

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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

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

https://github.com/richoux/microrts-uncertainty

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Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy games

https://github.com/richoux/taunt

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Terrain Analysis in StarCraft 1 and 2 as Combinatorial Optimization

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

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FACMAC: Factored Multi-Agent Centralised Policy Gradients

https://github.com/shariqiqbal2810/REFIL

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Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning

https://github.com/sisl/DICG

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Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning

https://github.com/smerdov/eSports_Sensors_Dataset

공식 코드

Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset

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

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