#multi-agent-games News & Analysis

Bilevel Optimization over Saddle Points of Zero-Sum Markov Games

Researchers propose PANDA, a novel bilevel optimization algorithm for reinforcement learning that handles competitive multi-agent scenarios modeled as zero-sum Markov games. The method achieves state-of-the-art convergence rates without requiring second-order derivatives, advancing RL applications in incentive design and competitive environments.