arXiv cs.AI
· Papers
Conservative Equilibrium Discovery in Offline Game-Theoretic Multiagent Reinforcement Learning
arXiv:2603.00374v2 Announce Type: replace Abstract: Offline learning of strategies takes data efficiency to its extreme by restricting algorithms to a fixed dataset of state-action trajectories. We consider the problem in a mixed-motive multiagent setting, where the goal is to solve a game under the offline learning co