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Found 4 papers, 2 papers with code
Inducing Cooperation via Learning to reshape rewards in semi-cooperative multi-agent reinforcement learning
Under these semi-cooperative scenarios, popular methods of centralized training with decentralized execution for inducing cooperation and removing the non-stationarity problem do not work well due to lack of a common shared reward as well as inscalability in centralized training.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation
The beam search refines these policies on the fly by pruning branches that are unfavourably evaluated by a discriminator.
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
We explore value-based solutions for multi-agent reinforcement learning (MARL) tasks in the centralized training with decentralized execution (CTDE) regime popularized recently.
Ranked #1 on
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Multi-agent Reinforcement Learning
reinforcement-learning
+2
Learning to Schedule Communication in Multi-agent Reinforcement Learning
Many real-world reinforcement learning tasks require multiple agents to make sequential decisions under the agents' interaction, where well-coordinated actions among the agents are crucial to achieve the target goal better at these tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
