Approximate Equilibrium

Approximate equilibrium analysis focuses on finding solutions in game-theoretic settings where perfect equilibrium may be computationally intractable or unrealistic due to uncertainties or incomplete information. Current research emphasizes robust solutions in multi-agent reinforcement learning (MARL) frameworks, often employing distributionally robust Markov games and algorithms like robust multi-agent Q-learning or actor-critic methods to handle uncertainties in states, rewards, or opponent strategies. This work is significant for improving the robustness and efficiency of MARL algorithms in real-world applications, such as security games and energy markets, where uncertainties and strategic interactions are prevalent. The development of efficient algorithms for finding approximate equilibria with provable guarantees is a key focus.