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A fundamental challenge in multiagent reinforcement learning is to learnbeneficial behaviors in a shared environment with other simultaneously learningagents. In particular, each agent perceives the environment as effectivelynon-stationary due to the changing policies of other agents. Moreover, eachagent is itself constantly learning, leading to natural non-stationarity in thedistribution of experiences encountered. In this paper, we propose a novelmeta-multiagent policy gradient theorem that directly accounts for thenon-stationary policy dynamics inherent to multiagent learning settings. Thisis achieved by modeling our gradient updates to consider both an agent's ownnon-stationary policy dynamics and the non-stationary policy dynamics of otheragents in the environment. We show that our theoretically grounded approachprovides a general solution to the multiagent learning problem, whichinherently comprises all key aspects of previous state of the art approaches onthis topic. We test our method on a diverse suite of multiagent benchmarks anddemonstrate a more efficient ability to adapt to new agents as they learn thanbaseline methods across the full spectrum of mixed incentive, competitive, andcooperative domains.

A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning