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Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability
Shayegan Omidshafiei · Jason Pazis · Chris Amato · Jonathan How · John L Vian

Tue Aug 08 01:30 AM -- 05:00 AM (PDT) @ Gallery #140

Many real-world tasks involve multiple agents with partial observability and limited communication. Learning is challenging in these settings due to local viewpoints of agents, which perceive the world as non-stationary due to concurrently-exploring teammates. Approaches that learn specialized policies for individual tasks face problems when applied to the real world: not only do agents have to learn and store distinct policies for each task, but in practice identities of tasks are often non-observable, making these approaches inapplicable. This paper formalizes and addresses the problem of multi-task multi-agent reinforcement learning under partial observability. We introduce a decentralized single-task learning approach that is robust to concurrent interactions of teammates, and present an approach for distilling single-task policies into a unified policy that performs well across multiple related tasks, without explicit provision of task identity.

Author Information

Shayegan Omidshafiei (MIT)
Jason Pazis (Amazon)
Chris Amato (Northeastern University)
Jonathan How (MIT)
John L Vian (The Boeing Company)

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