- See Also
-
Links
- “Player of Games”, Schmid et al 2021
- “Measuring Skill and Chance in Games”, Duersch et al 2020
- “ReBeL: Combining Deep Reinforcement Learning and Search for Imperfect-Information Games”, Brown et al 2020
- “Approximate Exploitability: Learning a Best Response in Large Games”, Timbers et al 2020
- “Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms”, Zhang et al 2019
- “Pluribus: Superhuman AI for Multiplayer Poker”, Brown & Sandholm 2019
- “NeuRD: Neural Replicator Dynamics”, Hennes et al 2019
- “Α-Rank: Multi-Agent Evaluation by Evolution”, Omidshafiei et al 2019
- “Deep Counterfactual Regret Minimization”, Brown et al 2018
- “Actor-Critic Policy Optimization in Partially Observable Multiagent Environments”, Srinivasan et al 2018
- “Safe and Nested Subgame Solving for Imperfect-Information Games”, Brown & Sandholm 2017
- “DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker”, Moravčík et al 2017
- “Equilibrium Approximation Quality of Current No-Limit Poker Bots”, Lisy & Bowling 2016
- “Deep Reinforcement Learning from Self-Play in Imperfect-Information Games”, Heinrich & Silver 2016
- “Non-Cooperative Games”, Nash 1951
- Wikipedia
- Link Bibliography
See Also
Links
“Player of Games”, Schmid et al 2021
“Measuring Skill and Chance in Games”, Duersch et al 2020
“ReBeL: Combining Deep Reinforcement Learning and Search for Imperfect-Information Games”, Brown et al 2020
“ReBeL: Combining Deep Reinforcement Learning and Search for Imperfect-Information Games”
“Approximate Exploitability: Learning a Best Response in Large Games”, Timbers et al 2020
“Approximate exploitability: Learning a best response in large games”
“Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms”, Zhang et al 2019
“Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms”
“Pluribus: Superhuman AI for Multiplayer Poker”, Brown & Sandholm 2019
“NeuRD: Neural Replicator Dynamics”, Hennes et al 2019
“Α-Rank: Multi-Agent Evaluation by Evolution”, Omidshafiei et al 2019
“Deep Counterfactual Regret Minimization”, Brown et al 2018
“Actor-Critic Policy Optimization in Partially Observable Multiagent Environments”, Srinivasan et al 2018
“Actor-Critic Policy Optimization in Partially Observable Multiagent Environments”
“Safe and Nested Subgame Solving for Imperfect-Information Games”, Brown & Sandholm 2017
“Safe and Nested Subgame Solving for Imperfect-Information Games”
“DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker”, Moravčík et al 2017
“DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker”
“Equilibrium Approximation Quality of Current No-Limit Poker Bots”, Lisy & Bowling 2016
“Equilibrium Approximation Quality of Current No-Limit Poker Bots”
“Deep Reinforcement Learning from Self-Play in Imperfect-Information Games”, Heinrich & Silver 2016
“Deep Reinforcement Learning from Self-Play in Imperfect-Information Games”
“Non-Cooperative Games”, Nash 1951
Wikipedia
Link Bibliography
-
https://arxiv.org/abs/2112.03178#deepmind: “Player of Games”,