4 accepted papers
Federated Deep Reinforcement Learning (FDRL) aims to enable distributed collaborative training of multiple DRL models while preserving privacy. Existing FDRL methods function in static client environments, but real-world scenarios often involve dynamic state transitions, such as noise, which render
Understanding how long-term stimulation reshapes neural circuits requires uncovering the rules of brain plasticity. While short-term synaptic modifications have been extensively characterized, the principles that drive circuit-level reorganization across hours to weeks remain unknown. Here, we forma…
Deep reinforcement learning (DRL) has been widely applied to various applications, but improving exploration remains a key challenge. Recently, multi-actor DRL has emerged as a promising approach that enhances exploration by simultaneously deploying multiple actors for learning. Among these methods,…
To avoid dangerous situations, such as collisions in dynamic environments, autonomous vehicles must predict the risks of the current scene to take safe actions. Traditional rule-based risk prediction methods and existing reinforcement learning (RL) approaches, which typically rely on manually design