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Reward Design in Cooperative Multi-agent Reinforcement Learning for Packet Routing
In this paper, we study reward design problem in cooperative MARL based on packet routing environments.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Learning Agent Communication under Limited Bandwidth by Message Pruning
We evaluate the gating mechanism on several tasks.
Learning Multi-agent Communication under Limited-bandwidth Restriction for Internet Packet Routing
Communication is an important factor for the big multi-agent world to stay organized and productive.
Modelling the Dynamic Joint Policy of Teammates with Attention Multi-agent DDPG
Second, to model the teammates' policies using the collected information in an effective way, ATT-MADDPG enhances the centralized critic with an attention mechanism.
ACCNet: Actor-Coordinator-Critic Net for "Learning-to-Communicate" with Deep Multi-agent Reinforcement Learning
Communication is a critical factor for the big multi-agent world to stay organized and productive.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
