4 accepted papers
Deep reinforcement learning (DRL) methods have been widely applied in distributed multi-robotic systems and successfully realized autonomous learning in many fields. In these fields, robots need to communicate and collaborate with other robots in real time, and reach agreed cognition for task assign
Deep reinforcement learning has achieved encouraging performance in many realms. However, one of its primary challenges is the sparsity of extrinsic rewards, which is still far from solved. Complementary learning system theory suggests that effective human learning relies on two complementary learni…
Multi-agent reinforcement learning methods have been widely used for multi-robotic systems, and meta-learning methods are also applied to help robots reuse prior experiences to guide new tasks learning. But in some multi-robotic tasks, the robot types cannot be determined in advance or may dynamical
Deep reinforcement learning has been demonstrated to be an effective solution to the multi-robot collision avoidance problem. However, with existing methods, robots typically generate actions only based on local observations, sometimes augmented with global communication. Their performance deteriora