Learning in Bi-level Markov Games

Although multi-agent reinforcement learning (MARL) has demonstrated remarkable progress in tackling sophisticated cooperative tasks, the assumption that agents take simultaneous actions still limits the applicability of MARL for many real-world problems. In this work, we relax the assumption by proposing the framework of the bi-level Markov game (BMG). BMG breaks the simultaneity by assigning two players with a leader-follower relationship in which the leader considers the policy of the follower who is taking the best response based on the leader's actions. We propose two provably convergent algorithms to solve BMG: BMG-1 and BMG-2. The former uses the standard Q-learning, while the latter relieves solving the local Stackelberg equilibrium in BMG-1 with the further two-step transition to estimate the state value. For both methods, we consider temporal difference learning techniques with both tabular and neural network representations. To verify the effectiveness of our BMG framework, we test on a series of games, including Seeker, Cooperative Navigation, and Football, that are challenging to existing MARL solvers find challenging to solve: Seeker, Cooperative Navigation, and Football. Experimental results show that our BMG methods achieve competitive advantages in terms of better performance and lower variance.