A Survey on Collaborative Learning for Intelligent Autonomous Systems
JCSD Anjos, KJ Matteussi, FC Orlandi… - ACM Computing …, 2023 - dl.acm.org
This survey examines approaches to promote Collaborative Learning in distributed systems
for emergent Intelligent Autonomous Systems (IAS). The study involves a literature review of …
for emergent Intelligent Autonomous Systems (IAS). The study involves a literature review of …
A comprehensive survey on multi-agent reinforcement learning for connected and automated vehicles
Connected and automated vehicles (CAVs) require multiple tasks in their seamless
maneuverings. Some essential tasks that require simultaneous management and actions …
maneuverings. Some essential tasks that require simultaneous management and actions …
Credit assignment in heterogeneous multi-agent reinforcement learning for fully cooperative tasks
Credit assignment poses a significant challenge in heterogeneous multi-agent
reinforcement learning (MARL) when tackling fully cooperative tasks. Existing MARL …
reinforcement learning (MARL) when tackling fully cooperative tasks. Existing MARL …
Recent advances in reinforcement learning-based autonomous driving behavior planning: A survey
Autonomous driving (AD) holds the potential to revolutionize transportation efficiency, but its
success hinges on robust behavior planning (BP) mechanisms. Reinforcement learning (RL) …
success hinges on robust behavior planning (BP) mechanisms. Reinforcement learning (RL) …
Finite-sample guarantees for nash Q-learning with linear function approximation
P Cisneros-Velarde, S Koyejo - Uncertainty in Artificial …, 2023 - proceedings.mlr.press
Nash Q-learning may be considered one of the first and most known algorithms in multi-
agent reinforcement learning (MARL) for learning policies that constitute a Nash equilibrium …
agent reinforcement learning (MARL) for learning policies that constitute a Nash equilibrium …
Resilient multi-agent RL: introducing DQ-RTS for distributed environments with data loss
This paper proposes DQ-RTS, a novel decentralized Multi-Agent Reinforcement Learning
algorithm designed to address challenges posed by non-ideal communication and a varying …
algorithm designed to address challenges posed by non-ideal communication and a varying …
Multi-UAV Path Planning and Following Based on Multi-Agent Reinforcement Learning
X Zhao, R Yang, L Zhong, Z Hou - Drones, 2024 - mdpi.com
Dedicated to meeting the growing demand for multi-agent collaboration in complex
scenarios, this paper introduces a parameter-sharing off-policy multi-agent path planning …
scenarios, this paper introduces a parameter-sharing off-policy multi-agent path planning …
Particle swarm optimization based leader-follower cooperative control in multi-agent systems
X Wang, D Yang, S Chen - Applied Soft Computing, 2024 - Elsevier
Multi-agent systems (MAS) have attracted significant attention in recent years due to their
wide applications in cooperative control, formation control, synchronization of complex …
wide applications in cooperative control, formation control, synchronization of complex …
Multiagent Reinforcement Learning: Methods, Trustworthiness, Applications in Intelligent Vehicles, and Challenges
Multiagent Reinforcement Learning (MARL) plays a pivotal role in intelligent vehicle
systems, offering solutions for complex decision-making, coordination, and adaptive …
systems, offering solutions for complex decision-making, coordination, and adaptive …
Multiagent Manuvering with the Use of Reinforcement Learning
M Orłowski, P Skruch - Electronics, 2023 - mdpi.com
This paper presents an approach for defining, solving, and implementing dynamic
cooperative maneuver problems in autonomous driving applications. The formulation of …
cooperative maneuver problems in autonomous driving applications. The formulation of …