A survey of progress on cooperative multi-agent reinforcement learning in open environment
Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …
has made progress in various fields. Specifically, cooperative MARL focuses on training a …
On Stateful Value Factorization in Multi-Agent Reinforcement Learning
Value factorization is a popular paradigm for designing scalable multi-agent reinforcement
learning algorithms. However, current factorization methods make choices without full …
learning algorithms. However, current factorization methods make choices without full …
Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning
This work focuses on equilibrium selection in no-conflict multi-agent games, where we
specifically study the problem of selecting a Pareto-optimal equilibrium among several …
specifically study the problem of selecting a Pareto-optimal equilibrium among several …
eDA3-X: Distributed Attentional Actor Architecture for Interpretability of Coordinated Behaviors in Multi-Agent Systems
Y Motokawa, T Sugawara - Applied Sciences, 2023 - mdpi.com
In this paper, we propose an enhanced version of the distributed attentional actor
architecture (eDA3-X) for model-free reinforcement learning. This architecture is designed to …
architecture (eDA3-X) for model-free reinforcement learning. This architecture is designed to …
An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning
C Amato - arXiv preprint arXiv:2409.03052, 2024 - arxiv.org
Multi-agent reinforcement learning (MARL) has exploded in popularity in recent years. Many
approaches have been developed but they can be divided into three main types: centralized …
approaches have been developed but they can be divided into three main types: centralized …
(A Partial Survey of) Decentralized, Cooperative Multi-Agent Reinforcement Learning
C Amato - arXiv preprint arXiv:2405.06161, 2024 - arxiv.org
Multi-agent reinforcement learning (MARL) has exploded in popularity in recent years. Many
approaches have been developed but they can be divided into three main types: centralized …
approaches have been developed but they can be divided into three main types: centralized …
Centralized vs. Decentralized Multi-Agent Reinforcement Learning for Enhanced Control of Electric Vehicle Charging Networks
A Shojaeighadikolaei, Z Talata, M Hashemi - arXiv preprint arXiv …, 2024 - arxiv.org
The widespread adoption of electric vehicles (EVs) poses several challenges to power
distribution networks and smart grid infrastructure due to the possibility of significantly …
distribution networks and smart grid infrastructure due to the possibility of significantly …
[PDF][PDF] Assessing the optimality of decentralized inspection and maintenance policies for stochastically degrading engineering systems
P Bhustali, P Charalampos - Assessing the Optimality of …, 2023 - research.tudelft.nl
Long-term inspection and maintenance (I&M) planning, a multi-stage stochastic optimization
problem, can be efficiently formulated as a partially observable Markov decision process …
problem, can be efficiently formulated as a partially observable Markov decision process …
MODT: Multi-Objective Database Tuner Using Hierarchical Reinforcement Learning
Index recommendation and knob tuning are two important database tuners. Despite
substantial progress in each of them, how these tuners together affect the overall database …
substantial progress in each of them, how these tuners together affect the overall database …
Machine Learning Algorithms for Energy Trading of Battery Energy Storage Systems: Reinforcement learning for trading energy on dual electricity markets
A Haratian - 2024 - diva-portal.org
The battery energy storage system (BESS) holds the promise of becoming an essential
element in our energy landscape. With the increasing need for renewable energy and …
element in our energy landscape. With the increasing need for renewable energy and …