Metadata-Version: 2.1
Name: SC2MM
Version: 0.0.1
Summary: A package for StarCraft2 Micro-Management.
Home-page: https://github.com/caiyangcy/SC2DC
Author: Cai Yang
Author-email: u6625166@anu.edu.au
License: UNKNOWN
Description: # SC2DC
        # StarCraft 2 Decentralized Control
        
        A collection of helpful papers for SC2 decentralized control, mainly focused on:
        * Micro Management
        * Communication
        * Planning
        * Cooperation
        * Coordination
        
        ## Review Paper
        * [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://arxiv.org/pdf/1911.10635.pdf)
        * [A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft](https://www.cs.mun.ca/~dchurchill/pdf/starcraft_survey.pdf)
        
        ## Research Paper
        
        * [Mutiagent Bidirectionally-Coordinated Nets Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games](https://arxiv.org/pdf/1703.10069.pdf)
        * [StarCraft II Build Order Optimization using Deep Reinforcement Learning and Monte-Carlo Tree Search](https://arxiv.org/pdf/2006.10525.pdf)
        * [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://arxiv.org/pdf/1803.11485.pdf)
        * [Efficient Communication in Multi-AgentReinforcement Learning via Variance Based Control](https://papers.nips.cc/paper/8586-efficient-communication-in-multi-agent-reinforcement-learning-via-variance-based-control.pdf)
        * [Deep Multi-Agent Reinforcement Learning for Decentralised Continuous Cooperative Control](https://arxiv.org/pdf/2003.06709.pdf)
        * [Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks](https://arxiv.org/pdf/1609.02993v3.pdf)
        * [Deep Coordination Graphs](https://arxiv.org/pdf/1910.00091.pdf)
        * [The StarCraft Multi-Agent Challenge](https://arxiv.org/pdf/1902.04043.pdf)
        * [An Analysis of Model-Based Heuristic Search Techniques for StarCraft Combat Scenarios](https://pdfs.semanticscholar.org/376b/ff162fc0143642cc7fa7d3547eef48871b51.pdf)
        * [Multi-scale Bayesian modeling for RTS games: an application to StarCraft AI](https://hal.archives-ouvertes.fr/hal-01228961/document)
        * [Learning to Select Actions in Starcraft with Genetic Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7880180)
        * [Neuroevolution for RTS Micro](https://arxiv.org/pdf/1803.10288.pdf)
        * [Real-time challenge balance in an RTS game using rtNEAT](https://www.csse.uwa.edu.au/cig08/Proceedings/papers/8023.pdf)
        * [Neuroevolution Based Multi-Agent System with Ontology Based Template Creation for Micromanagement in Real-Time Strategy Games](https://pdfs.semanticscholar.org/0197/8064174712b42f5c14c01afcba30a736bbfd.pdf)
        * [Potential-Field-Based Unit Behavior Optimization for Balancing in StarCraft II](https://dl.acm.org/doi/pdf/10.1145/2739482.2764643)
        * [Comparing Three Approaches to Micro in RTS Games](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8790308&tag=1)
        * [Attentive Relational State Representation in Decentralized Multiagent Reinforcement Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9049415&tag=1)
        * [Spellcaster Control Agent in StarCraft II Using Deep Reinforcement Learning]()
        * [Learning to Select Actions in StarCraft with Genetic Algorithms](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7880180&tag=1)
        * [Comparing Three Approaches to Micro in RTS Games](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8790308&tag=1)
        * [Neuroevolution for RTS Micro](https://arxiv.org/pdf/1803.10288.pdf)
        * [Neuroevolution Based Multi-Agent System with Ontology Based Template Creation for Micromanagement in Real-Time Strategy Games](https://pdfs.semanticscholar.org/0197/8064174712b42f5c14c01afcba30a736bbfd.pdf)
        * [Evolving Neural Networks through Augmenting Topologies](https://dl.acm.org/doi/pdf/10.1162/106365602320169811)
        
        ## Thesis
        * [Heuristic Search Techniques for Real-Time Strategy Games](http://www.cs.mun.ca/~dchurchill/pdf/DavidChurchill_phd_thesis.pdf)
        
        ## Environment / Simulation / API
        * [pysc2](https://github.com/deepmind/pysc2)
        * [SparCraft](https://github.com/davechurchill/ualbertabot/wiki/SparCraft-Home)
        * [Brood War API](https://github.com/bwapi/bwapi)
        * [SMAC](https://github.com/oxwhirl/smac)
        
        ## Maps
        * You can find a list of maps [here](https://github.com/caiyangcy/SC2DC/blob/master/docs/map_info.md)
        
        # Run
        
        To get started:
        
            python -m scdc.agents.scripted..agent_demo
            
        # Acknowledgement
        * The coding is based on [SMAC](https://github.com/oxwhirl/smac). Refer to the repo for details and license.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6.10
Description-Content-Type: text/markdown
