Deep decentralized multi-task multi-agent reinforcement learning under partial observability
S Omidshafiei, J Pazis, C Amato… - … on Machine Learning, 2017 - proceedings.mlr.press
Many real-world tasks involve multiple agents with partial observability and limited
communication. Learning is challenging in these settings due to local viewpoints of agents …
communication. Learning is challenging in these settings due to local viewpoints of agents …
Lenient multi-agent deep reinforcement learning
Much of the success of single agent deep reinforcement learning (DRL) in recent years can
be attributed to the use of experience replay memories (ERM), which allow Deep Q …
be attributed to the use of experience replay memories (ERM), which allow Deep Q …
Towards reinforcement learning-based aggregate computing
Recent trends in pervasive computing promote the vision of Collective Adaptive Systems
(CASs): large-scale collections of relatively simple agents that act and coordinate with no …
(CASs): large-scale collections of relatively simple agents that act and coordinate with no …
Adaptive learning: A new decentralized reinforcement learning approach for cooperative multiagent systems
ML Li, S Chen, J Chen - IEEE Access, 2020 - ieeexplore.ieee.org
Multiagent systems (MASs) have received extensive attention in a variety of domains, such
as robotics and distributed control. This paper focuses on how independent learners (ILs …
as robotics and distributed control. This paper focuses on how independent learners (ILs …
A meta optimisation analysis of particle swarm optimisation velocity update equations for watershed management learning
Particle swarm optimisation (PSO) is a general purpose optimisation algorithm used to
address hard optimisation problems. The algorithm operates as a result of a number of …
address hard optimisation problems. The algorithm operates as a result of a number of …
A new approach based on particle swarm optimization algorithm for solving data allocation problem
The effectiveness distributed database systems highly depends on the state of site that its
task is to allocate fragments. This allocation purpose is performed for obtaining the minimum …
task is to allocate fragments. This allocation purpose is performed for obtaining the minimum …
An agent-based intelligent algorithm for uniform machine scheduling to minimize total completion time
K Li, JYT Leung, BY Cheng - Applied Soft Computing, 2014 - Elsevier
This paper considers the uniform machine scheduling problem with release dates so as to
minimize the total completion time. The problem is known to be NP-hard in the strong sense …
minimize the total completion time. The problem is known to be NP-hard in the strong sense …
Cooperation in the evolutionary iterated prisoner's dilemma game with risk attitude adaptation
W Zeng, M Li, F Chen - Applied Soft Computing, 2016 - Elsevier
Abstract The Iterated Prisoner's Dilemma (IPD) game has been commonly used to
investigate the cooperation among competitors. However, most previous studies on the IPD …
investigate the cooperation among competitors. However, most previous studies on the IPD …
[PDF][PDF] Multi-Agent with Multi Objective-Based Optimized Resource Allocation on Inter-Cloud.
J Arravinth, D Manjula - Intelligent Automation & Soft Computing, 2022 - cdn.techscience.cn
Cloud computing is the ability to provide new technologies and standard cloud services.
One of the essential features of cloud computing is the provision of “unlimited” computer …
One of the essential features of cloud computing is the provision of “unlimited” computer …
An actor-critic-attention mechanism for deep reinforcement learning in multi-view environments
In reinforcement learning algorithms, leveraging multiple views of the environment can
improve the learning of complicated policies. In multi-view environments, due to the fact that …
improve the learning of complicated policies. In multi-view environments, due to the fact that …