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Learning Strategic Value and Cooperation in Multi-Player Stochastic Games through Side Payments

arXiv 2303.05307 ↗First posted 9 Mar 2023cs.GT cs.AIcited by 0

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Abstract (arXiv)

We study general-sum, multi-player stochastic games with transferable utility, motivated by settings where agents can use side payments to make cooperation individually rational. Building on the Harsanyi--Shapley (HS) value for normal-form games, we introduce two HS-based value notions for stochastic games: HS-S, defined by aggregating dynamic coalition-versus-complement threat powers, and Coco-S, defined as fixed points of a statewise HS Bellman operator. We extend HS-style axioms to the stochastic setting and show that HS-S is the unique mapping satisfying them. We prove that HS-S and Coco-S coincide in all two-player stochastic games, but can disagree when n>2, via an explicit three-player counterexample. We prove existence and uniqueness of Coco-S fixed points for all two-player games and for three-player two-state games via topological degree theory, and provide an axiomatic characterization of Coco-S through a new Markov Consistency axiom that distinguishes it from HS-S. Finally, we give sampling-based estimators with finite-sample guarantees and empirically compare the induced values, policies, and side payments on multi-player grid-game benchmarks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 31 references that are in the map. First posted on arXiv 9 Mar 2023.

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12 of 31 references.

References · 12

  1. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 903
  2. 2018A Survey and Critique of Multiagent Deep Reinforcement Learningcited by 173
  3. 2020Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibriumcited by 100
  4. 2020Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexitycited by 91
  5. 2021When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently?cited by 63
  6. 2020Learning to Incentivize Other Learning Agentscited by 55
  7. 2021Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Gamescited by 42
  8. 2021On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learningcited by 35
  9. 2018Evolving intrinsic motivations for altruistic behaviorcited by 30
  10. 2021On the Complexity of Computing Markov Perfect Equilibrium in General-Sum Stochastic Gamescited by 18
  11. 2019Deep Q-Learning for Nash Equilibria: Nash-DQNcited by 14
  12. 2020Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Gamescited by 9

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