Multi-Agent Spacecraft Docking with Reinforcement Learning This research explores the application of Multi-Agent Reinforcement Learning (MARL) to a cooperative spacecraft docking problem with three chaser spacecraft and a lightly tumbling
Multi-Agent Reinforcement Learning Empower Space Unmanned Systems . . . Leveraging its distributed decision-making architecture and co-evolutionary mechanisms, multi-agent reinforcement learning (MARL) offers a breakthrough solution for building autonomous and resilient intelligent space systems
AAS 23-026 PLANNING AUTONOMOUS SPACECRAFT RENDEZVOUS AND DOCKING . . . (PPO) Reinforcement Learning algo-rithm for three-dimensional autonomous spacecraft trajectory planning Specifically, we consider a ch ser spacecraft performing a rendezvous and docking mission with a target spacecraft on a circular orbit This reinforcement learning approach utilizes an actor and critic method to plan safe trajectories for the c
Multi-Agent Reinforcement Learning for Explainable Spacecraft . . . This work presents an attention-based multi-agent reinforcement learning (MARL) framework for spacecraft configuration design, formulated as a sequential decision process with hard geometric constraints and five competing physical objectives
MARLIN: Multi-Agent Reinforcement Learning for Incremental DAG Discovery To address these challenges, in this paper, we propose MARLIN, an efficient multi-agent reinforcement learning framework for incremental DAG learning We first develop an efficient intra-batch DAG learning method that maps from a continuous real-valued space to the DAG space with-out enforcing an acyclicity constraint
Mean-Field Deep Reinforcement Learning for Multi-Agent Path Finding Continuous-space multi-agent path finding (MAPF) presents severe challenges for deep reinforcement learning (DRL), as joint state–action spaces grow exponentially and fine-grained inter-agent coordination is required