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In the last decade, there have been significant advances in multi-agentreinforcement learning (MARL) but there are still numerous challenges, such ashigh sample complexity and slow convergence to stable policies, that need to beovercome before wide-spread deployment is possible. However, many real-worldenvironments already, in practice, deploy sub-optimal or heuristic approachesfor generating policies. An interesting question that arises is how to best usesuch approaches as advisors to help improve reinforcement learning inmulti-agent domains. In this paper, we provide a principled framework forincorporating action recommendations from online sub-optimal advisors inmulti-agent settings. We describe the problem of ADvising Multiple IntelligentReinforcement Agents (ADMIRAL) in nonrestrictive general-sum stochastic gameenvironments and present two novel Q-learning based algorithms: ADMIRAL -Decision Making (ADMIRAL-DM) and ADMIRAL - Advisor Evaluation (ADMIRAL-AE),which allow us to improve learning by appropriately incorporating advice froman advisor (ADMIRAL-DM), and evaluate the effectiveness of an advisor(ADMIRAL-AE). We analyze the algorithms theoretically and provide fixed-pointguarantees regarding their learning in general-sum stochastic games.Furthermore, extensive experiments illustrate that these algorithms: can beused in a variety of environments, have performances that compare favourably toother related baselines, can scale to large state-action spaces, and are robustto poor advice from advisors.

Multi-Agent Advisor Q-Learning