Leader-Follower Network Aggregative Game with Stochastic Agents' Communication and Activeness
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Abstract (arXiv)
This technical note presents a leader-follower scheme for network aggregative games. The followers and leader are selfish cost minimizing agents. The cost function of each follower is affected by strategy of leader and aggregated strategies of its neighbors through a communication graph. The leader infinitely often wakes up and receives the aggregated strategy of the followers, updates its decision value and broadcasts it to all the followers. Then, the followers apply the updated strategy of the leader into their cost functions. The establishment of information exchange between each neighboring pair of followers, and the activeness of each follower to update its decision at each iteration are both considered to be drawn from two arbitrary distributions. Moreover, a distributed algorithm based on subgradient method is proposed for updating the strategies of leader and followers. The convergence of the proposed algorithm to the unique generalized Nash equilibrium point of the game is proven in both almost sure and mean square senses.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Aug 2019.
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Citing papers per year: 2023: 1; 2024: 3; 2025: 1.
5 papers.
- 2024Risk-Sensitive Multi-Agent Reinforcement Learning in Network Aggregative Markov Gamescited by 1
- 2024On the Stability of Learning in Network Games with Many Playerscited by 1
- 2025Convergence and Connectivity: Dynamics of Multi-Agent Q-Learning in Random Networkscited by 1
- 2023Stability of Multi-Agent Learning in Competitive Networks: Delaying the Onset of Chaoscited by 0
- 2024Risk Sensitivity in Markov Games and Multi-Agent Reinforcement Learning: A Systematic Reviewcited by 0
- 2025Convergence and Connectivity: Dynamics of Multi-Agent Q-Learning in Random Networks13 Mar 2025
- 2024Risk Sensitivity in Markov Games and Multi-Agent Reinforcement Learning: A Systematic Review10 Jun 2024
- 2024On the Stability of Learning in Network Games with Many Players23 Mar 2024
- 2024Risk-Sensitive Multi-Agent Reinforcement Learning in Network Aggregative Markov Games8 Feb 2024
- 2023Stability of Multi-Agent Learning in Competitive Networks: Delaying the Onset of Chaos19 Dec 2023