Asynchronous multi-agent deep reinforcement learning under partial observability

The state-of-the-art multi-agent reinforcement learning (MARL) methods provide promising solutions to a variety of complex problems. Yet, these methods all assume that agents perform primitive actions in a synchronized manner, making them impractical for long-horizon real-world multi-robot tasks that inherently require robots to asynchronously reason about action selection at varying time durations. To solve this problem, we first propose a group of value-based cooperative MARL approaches for asynchronous execution using temporally extended macro-actions . Here, agents perform asynchronous learning and decision-making with macro-action-value functions in three paradigms: decentralized learning and control, centralized learning and control, and centralized training for decentralized execution (CTDE). Building on the above work, we formulate a set of macro-action-based policy gradient algorithms under the three training paradigms, where agents directly optimize their parameterized policies in an asynchronous manner. We evaluate our methods both in simulation and on real robots over a variety of realistic domains. Empirical results demonstrate the effectiveness of our algorithms for learning high-quality and asynchronous solutions with macro-actions in large multi-agent problems that were previously unsolvable via primitive-action-based approaches. The proposed approaches represent the first general MARL methods for temporally extended actions and serve as the foundation for future methods in the area.