Almost Sure Convergence of Networked Policy Gradient over Time-Varying Networks in Markov Potential Games
eess.SY, cs.SY, math.OC
Submitted: 2024-10-26
Updated: 2026-09-18
Terminology
Sources
- Learning Parametric Closed-Loop Policies for Markov Potential Games
- A Survey of Multi-Agent Deep Reinforcement Learning with Communication
- Multi-Agent Reinforcement Learning for Pragmatic Communication and Control
- Learning to Schedule Communication in Multi-agent Reinforcement Learning
- Networked Multi-Agent Reinforcement Learning with Emergent Communication
- Global Convergence of Multi-Agent Policy Gradient in Markov Potential Games
- On the convergence of policy gradient methods to Nash equilibria in general stochastic games
- RAPID: Autonomous Multi-Agent Racing using Constrained Potential Dynamic Games
- Learning to Share in Multi-Agent Reinforcement Learning
- On Characterizations of Potential and Ordinal Potential Games
- Markov Potential Game Construction and Multi-Agent Reinforcement Learning with Applications to Autonomous Driving
- Distributed Fictitious Play in Potential Games with Time-Varying Communication Networks
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