Showing eess.SYShow all
3 papers · 1 filter
eess.SY2024
Maximum Causal Entropy IRL in Mean-Field Games and GNEP Framework for Forward RL
Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi
This paper explores the use of Maximum Causal Entropy Inverse Reinforcement Learning (IRL) within the context of discrete-time stationary Mean-Field Games (MFGs) characterized by f…
eess.SY2019
Learning in Discounted-cost and Average-cost Mean-field Games
Berkay Anahtarcı, Can Deha Karıksız, Naci Saldi
We consider learning approximate Nash equilibria for discrete-time mean-field games with nonlinear stochastic state dynamics subject to both average and discounted costs. To this e…
eess.SY2019
Value Iteration Algorithm for Mean-field Games
Berkay Anahtarci, Can Deha Kariksiz, Naci Saldi
In the literature, existence of mean-field equilibria has been established for discrete-time mean field games under both the discounted cost and the average cost optimality criteri…