- See Also
-
Links
- “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”
- Wikipedia
- Miscellaneous
- Link Bibliography
See Also
Links
“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”
Wikipedia
Miscellaneous
Link 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”,