‘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 ”
- “The Unexpected Difficulty of Comparing AlphaStar to Humans ”
- “AlphaStar vs AlphaStar (PvP) & Dev Answered Questions! ”
- Wikipedia
- Miscellaneous
- Bibliography
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 ”
DeepMind Research on Ladder—StarCraft II
“The Unexpected Difficulty of Comparing AlphaStar to Humans ”
The unexpected difficulty of comparing AlphaStar to humans
“AlphaStar vs AlphaStar (PvP) & Dev Answered Questions! ”
Wikipedia
Miscellaneous
Bibliography
https://: “JaxMARL: Multi-Agent RL Environments in JAX ”,arxiv.org/ abs/ 2311.10090 https://: “TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full Game ”,arxiv.org/ abs/ 2011.13729#tencent https://: “TLeague: A Framework for Competitive Self-Play Based Distributed Multi-Agent Reinforcement Learning ”,arxiv.org/ abs/ 2011.12895#tencent 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 ”,