Conceitos essenciais
Satisficing paths in multi-agent reinforcement learning allow for exploration and convergence to equilibrium strategies.
Resumo
This paper explores the concept of satisficing paths in multi-agent reinforcement learning (MARL) algorithms. It studies sequences of strategies that satisfy a pairwise constraint, allowing for exploration while ensuring convergence to equilibrium strategies. The analysis focuses on normal-form games and their implications for MARL algorithms. The paper provides a positive answer to the question of constructing satisficing paths that terminate at Nash equilibrium in finite normal-form games. The proof involves constructing a path from an initial strategy profile to a Nash equilibrium by strategically switching strategies of unsatisfied players. The study highlights the importance of satisficing paths in decentralized learning and their potential for wider applications in game theory and MARL algorithms.
Introduction
- Game theory studies strategic interactions among self-interested agents.
- Multi-agent reinforcement learning (MARL) involves iterative strategy revisions.
- MARL algorithms aim to approximate dynamical systems on strategy profiles.
Satisficing Paths
- Satisficing paths allow for exploration while ensuring convergence to equilibrium.
- Sequences of strategies satisfying a pairwise constraint are termed satisficing paths.
- The concept of satisficing paths is crucial in MARL algorithms for convergence guarantees.
Main Result
- The paper proves that every finite normal-form game has the satisficing paths property.
- A satisficing path can be constructed from any initial strategy profile to a Nash equilibrium.
Discussion
- Satisficing paths offer a decentralized approach to learning in MARL algorithms.
- The complexity of computing satisficing paths and their dynamics are discussed.
Conclusion
- Satisficing paths provide a flexible and effective approach to convergence in MARL algorithms.
Estatísticas
"The length of such a path can be uniformly bounded above as T(x1) ≤n."
"There exists a collection of strategy update functions {f i Γ}n i=1."
Citações
"Satisficing paths can be interpreted as a natural generalization of best response paths."
"Multi-agent reinforcement learning algorithms based on the 'win stay, lose shift' principle are well suited to decentralized applications."