[PDF][PDF] Multi-Agent Reinforcement Learning Methods with Dynamic Parameters for Logistic Tasks
E Fedorov, O Nechyporenko, Y Korpan… - 2024 - ceur-ws.org
Part of Industry 4.0 is building computer systems by combining artificial intelligence with
robotics. Such computer systems play an important role in the planning of cargo …
robotics. Such computer systems play an important role in the planning of cargo …
Multi-agent reinforcement learning: A review of challenges and applications
In this review, we present an analysis of the most used multi-agent reinforcement learning
algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the …
algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the …
Multi-agent reinforcement learning: An overview
Multi-agent systems can be used to address problems in a variety of domains, including
robotics, distributed control, telecommunications, and economics. The complexity of many …
robotics, distributed control, telecommunications, and economics. The complexity of many …
A comprehensive survey of multiagent reinforcement learning
Multiagent systems are rapidly finding applications in a variety of domains, including
robotics, distributed control, telecommunications, and economics. The complexity of many …
robotics, distributed control, telecommunications, and economics. The complexity of many …
[PDF][PDF] Reinforcement learning for multi-agent systems
Multi-agent systems are rapidly finding applications in a variety of domains, including
robotics, distributed control, telecommunications, etc. Although the individual agents can be …
robotics, distributed control, telecommunications, etc. Although the individual agents can be …
Reinforcement learning and aggregation
J Jiang, M Kamel, L Chen - … Man and Cybernetics (IEEE Cat. No …, 2004 - ieeexplore.ieee.org
Reinforcement learning (RL) is a learning technique that provides a means for learning an
optimal control policy when the dynamics of the environment under consideration is …
optimal control policy when the dynamics of the environment under consideration is …
Multi-agent reinforcement learning: weighting and partitioning
R Sun, T Peterson - Neural networks, 1999 - Elsevier
This article addresses weighting and partitioning, in complex reinforcement learning tasks,
with the aim of facilitating learning. The article presents some ideas regarding weighting of …
with the aim of facilitating learning. The article presents some ideas regarding weighting of …
Transfer learning in multi-agent reinforcement learning domains
In the context of reinforcement learning, transfer learning refers to the concept of reusing
knowledge acquired in past tasks to speed up the learning procedure in new tasks. Transfer …
knowledge acquired in past tasks to speed up the learning procedure in new tasks. Transfer …
Lateral transfer learning for multiagent reinforcement learning
H Shi, J Li, J Mao, KS Hwang - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Some researchers have introduced transfer learning mechanisms to multiagent
reinforcement learning (MARL). However, the existing works devoted to cross-task transfer …
reinforcement learning (MARL). However, the existing works devoted to cross-task transfer …
[PDF][PDF] Heuristic selection of actions in multiagent reinforcement learning.
This work presents a new algorithm, called Heuristically Accelerated Minimax-Q (HAMMQ),
that allows the use of heuristics to speed up the wellknown Multiagent Reinforcement …
that allows the use of heuristics to speed up the wellknown Multiagent Reinforcement …