Nonconvergence to saddle boundary points under perturbed reinforcement learning
Författare
Summary, in English
For several reinforcement learning models in strategic-form games, convergence to action profiles that are not Nash equilibria may occur with positive probability under certain conditions on the payoff function. In this paper, we explore how an alternative reinforcement learning model, where the strategy of each agent is perturbed by a strategy-dependent perturbation (or mutations) function, may exclude convergence to non-Nash pure strategy profiles. This approach extends prior analysis on reinforcement learning in games that addresses the issue of convergence to saddle boundary points. It further provides a framework under which the effect of mutations can be analyzed in the context of reinforcement learning.
Avdelning/ar
Publiceringsår
2015
Språk
Engelska
Sidor
667-699
Publikation/Tidskrift/Serie
International Journal of Game Theory
Volym
44
Issue
3
Dokumenttyp
Artikel i tidskrift
Förlag
Springer
Ämne
- Control Engineering
Nyckelord
- Learning in games
- Reinforcement learning
- Replicator dynamics
Status
Published
ISBN/ISSN/Övrigt
- ISSN: 1432-1270