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Found 4 papers, 1 papers with code
Preventing Value Function Collapse in Ensemble {Q}-Learning by Maximizing Representation Diversity
Recently, the Maxmin and Ensemble Q-learning algorithms have used different estimates provided by the ensembles of learners to reduce the overestimation bias.
Multi-Agent Reinforcement Learning for Problems with Combined Individual and Team Reward
This is a challenging task for current state-of-the-art multi-agent reinforcement algorithms that are designed to either maximize the global reward of the team or the individual local rewards.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Universal Policies to Learn Them All
We explore a collaborative and cooperative multi-agent reinforcement learning setting where a team of reinforcement learning agents attempt to solve a single cooperative task in a multi-scenario setting.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Designing a Multi-Objective Reward Function for Creating Teams of Robotic Bodyguards Using Deep Reinforcement Learning
We are considering a scenario where a team of bodyguard robots provides physical protection to a VIP in a crowded public space.
