3 accepted papers
We introduce MOSDT, the first algorithm designed for multi-agent offline safe reinforcement learning (MOSRL), alongside MOSDB, the first dataset and benchmark for this domain. Different from most existing knowledge distillation-based multi-agent RL methods, we propose policy self-distillation (PSD)…
Collaborative perception in autonomous driving significantly enhances the perception capabilities of individual agents. Immutable heterogeneity in collaborative perception, where agents have different and fixed perception networks, presents a major challenge due to the semantic gap in their exchange…
"Sharing intermediate neural features enables agents to effectively see through occlusions. Due to agent diversity, some pioneering works have studied domain adaption for heterogeneous neural features. Nevertheless, these works all partially replace agents’ private neural network with newly trained…