‘AlphaStar’ directory
- See Also
- Links
- “AI Alignment via Slow Substrates: Early Empirical Results With StarCraft II”, Leong 2024
- “Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach”, Ma et al 2023
- “JaxMARL: Multi-Agent RL Environments in JAX”, Rutherford et al 2023
- “AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning”, Mathieu et al 2023
- “SCC: an Efficient Deep Reinforcement Learning Agent Mastering the Game of StarCraft II”, Wang et al 2020
- “TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full Game”, Han et al 2020
- “TLeague: A Framework for Competitive Self-Play Based Distributed Multi-Agent Reinforcement Learning”, Sun et al 2020
- “Real World Games Look Like Spinning Tops”, Czarnecki et al 2020
- “Grandmaster Level in StarCraft II Using Multi-Agent Reinforcement Learning”, Vinyals et al 2019
- “Human-Level Performance in 3D Multiplayer Games With Population-Based Reinforcement Learning”, Jaderberg et al 2019
- “Re-Evaluating Evaluation”, Balduzzi et al 2018
- “Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks”, Usunier et al 2016
- “Pointer Networks”, Vinyals et al 2015
- “AlphaStar: Grandmaster Level in StarCraft II Using Multi-Agent Reinforcement Learning”
- “AlphaStar: Mastering the Real-Time Strategy Game StarCraft II”
- “TLeague Project Page”
- “DeepMind Research on Ladder—StarCraft II”
- “Brief Notes on Pluto AI (SCBW)”
- “The Unexpected Difficulty of Comparing AlphaStar to Humans”
- “AlphaStar vs AlphaStar (PvP) & Dev Answered Questions!”
- Wikipedia (2)
- Miscellaneous
- Bibliography
See Also
Links
“AI Alignment via Slow Substrates: Early Empirical Results With StarCraft II”, Leong 2024
AI Alignment via Slow Substrates: Early Empirical Results With StarCraft II
“Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach”, Ma et al 2023
Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach
“JaxMARL: Multi-Agent RL Environments in JAX”, Rutherford et al 2023
“AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning”, Mathieu et al 2023
AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning
“SCC: an Efficient Deep Reinforcement Learning Agent Mastering the Game of StarCraft II”, Wang et al 2020
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II
“TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full Game”, Han et al 2020
“TLeague: A Framework for Competitive Self-Play Based Distributed Multi-Agent Reinforcement Learning”, Sun et al 2020
TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement Learning
“Real World Games Look Like Spinning Tops”, Czarnecki et al 2020
“Grandmaster Level in StarCraft II Using Multi-Agent Reinforcement Learning”, Vinyals et al 2019
Grandmaster level in StarCraft II using multi-agent reinforcement learning
“Human-Level Performance in 3D Multiplayer Games With Population-Based Reinforcement Learning”, Jaderberg et al 2019
Human-level performance in 3D multiplayer games with population-based reinforcement learning
“Re-Evaluating Evaluation”, Balduzzi et al 2018
“Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks”, Usunier et al 2016
“Pointer Networks”, Vinyals et al 2015
“AlphaStar: Grandmaster Level in StarCraft II Using Multi-Agent Reinforcement Learning”
AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning
“AlphaStar: Mastering the Real-Time Strategy Game StarCraft II”
AlphaStar: Mastering the Real-Time Strategy Game StarCraft II
“TLeague Project Page”
“DeepMind Research on Ladder—StarCraft II”
“Brief Notes on Pluto AI (SCBW)”
“The Unexpected Difficulty of Comparing AlphaStar to Humans”
“AlphaStar vs AlphaStar (PvP) & Dev Answered Questions!”
Wikipedia (2)
Miscellaneous
Bibliography
https://arxiv.org/abs/2311.10090: “JaxMARL: Multi-Agent RL Environments in JAX”,https://arxiv.org/abs/2011.13729#tencent: “TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full Game”,https://arxiv.org/abs/2011.12895#tencent: “TLeague: A Framework for Competitive Self-Play Based Distributed Multi-Agent Reinforcement Learning”,2019-vinyals.pdf#deepmind: “Grandmaster Level in StarCraft II Using Multi-Agent Reinforcement Learning”,2019-jaderberg.pdf#deepmind: “Human-Level Performance in 3D Multiplayer Games With Population-Based Reinforcement Learning”,