Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning

Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning

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Intro

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1 of 8

Intro

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Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning

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  1. 1 Intro
  2. 2 Motivations
  3. 3 Policy-Space Response Oracles (PSRO) [Lanctot et. al '17] • Maintains a pool of strategies for each player, and iteratively.
  4. 4 Motivated Example: "Deal-or-No-Deal"[1]
  5. 5 Example: Bach or Stravinsky
  6. 6 PSRO on games beyond purely adversarial domains (no search)
  7. 7 Extending AlphaZero to Large Imperfect Information
  8. 8 MCTS in PSRO: A Bayesian Interpretation

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