Deep decentralized multi-task multi-agent reinforcement learning under partial observability
Copyright © 2017 by the author(s). 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 concurrentlyexploring teammates. Appro...
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| Format: | Article |
| Language: | English |
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2021
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| Online Access: | https://hdl.handle.net/1721.1/137943 |