- See Also
-
Links
- “DeepNash: Mastering the Game of Stratego With Model-Free Multiagent Reinforcement Learning”, Et Al 2022
- “DouZero: Mastering DouDizhu With Self-Play Deep Reinforcement Learning”, Et Al 2021
- “Suphx: Mastering Mahjong With Deep Reinforcement Learning”, Et Al 2020
- “From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization”, Et Al 2020
- “Finding Friend and Foe in Multi-Agent Games”, Et Al 2019
- “Monte Carlo Neural Fictitious Self-Play: Approach to Approximate Nash Equilibrium of Imperfect-Information Games”, Et Al 2019
- “A Survey and Critique of Multiagent Deep Reinforcement Learning”, Hernandez- Et Al 2018
- “Solving Imperfect-Information Games via Discounted Regret Minimization”, 2018
- “ExIt-OOS: Towards Learning from Planning in Imperfect Information Games”, 2018
- “Regret Minimization for Partially Observable Deep Reinforcement Learning”, Et Al 2017
- “LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions”, Et Al 2017
- “Deep Recurrent Q-Learning for Partially Observable MDPs”, 2015
- “One Writer Enters International Competition to Play the World-conquering Game That Redefines What It Means to Be a Geek (and a Person)”
- Wikipedia
- Miscellaneous
- Link Bibliography
See Also
Links
“DeepNash: Mastering the Game of Stratego With Model-Free Multiagent Reinforcement Learning”, Et Al 2022
“DeepNash: Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning”, 2022-06-30 (; similar; bibliography)
“DouZero: Mastering DouDizhu With Self-Play Deep Reinforcement Learning”, Et Al 2021
“DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning”, 2021-06-11 (similar)
“Suphx: Mastering Mahjong With Deep Reinforcement Learning”, Et Al 2020
“Suphx: Mastering Mahjong with Deep Reinforcement Learning”, 2020-03-30 (similar)
“From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization”, Et Al 2020
“From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization”, 2020-02-19 (similar)
“Finding Friend and Foe in Multi-Agent Games”, Et Al 2019
“Finding Friend and Foe in Multi-Agent Games”, 2019-06-05 (; similar)
“Monte Carlo Neural Fictitious Self-Play: Approach to Approximate Nash Equilibrium of Imperfect-Information Games”, Et Al 2019
“Monte Carlo Neural Fictitious Self-Play: Approach to Approximate Nash equilibrium of Imperfect-Information Games”, 2019-03-22 (similar)
“A Survey and Critique of Multiagent Deep Reinforcement Learning”, Hernandez- Et Al 2018
“A Survey and Critique of Multiagent Deep Reinforcement Learning”, 2018-10-12 (; similar)
“Solving Imperfect-Information Games via Discounted Regret Minimization”, 2018
“Solving Imperfect-Information Games via Discounted Regret Minimization”, 2018-09-11 (similar)
“ExIt-OOS: Towards Learning from Planning in Imperfect Information Games”, 2018
“ExIt-OOS: Towards Learning from Planning in Imperfect Information Games”, 2018-08-30 (; similar)
“Regret Minimization for Partially Observable Deep Reinforcement Learning”, Et Al 2017
“Regret Minimization for Partially Observable Deep Reinforcement Learning”, 2017-10-31 (similar)
“LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions”, Et Al 2017
“LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions”, 2017-08-18 (; similar)
“Deep Recurrent Q-Learning for Partially Observable MDPs”, 2015
“Deep Recurrent Q-Learning for Partially Observable MDPs”, 2015-07-23 (; similar)
“One Writer Enters International Competition to Play the World-conquering Game That Redefines What It Means to Be a Geek (and a Person)”
Wikipedia
Miscellaneous
Link Bibliography
-
https://arxiv.org/abs/2206.15378#deepmind: “DeepNash: Mastering the Game of Stratego With Model-Free Multiagent Reinforcement Learning”, :