A survey on reinforcement learning methods in character animation

A Kwiatkowski, E Alvarado, V Kalogeiton… - Computer Graphics …, 2022 - Wiley Online Library
Reinforcement Learning is an area of Machine Learning focused on how agents can be
trained to make sequential decisions, and achieve a particular goal within an arbitrary …

A review of cooperation in multi-agent learning

Y Du, JZ Leibo, U Islam, R Willis, P Sunehag - arXiv preprint arXiv …, 2023 - arxiv.org
Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous
disciplines, including game theory, economics, social sciences, and evolutionary biology …

Qplex: Duplex dueling multi-agent q-learning

J Wang, Z Ren, T Liu, Y Yu, C Zhang - arXiv preprint arXiv:2008.01062, 2020 - arxiv.org
We explore value-based multi-agent reinforcement learning (MARL) in the popular
paradigm of centralized training with decentralized execution (CTDE). CTDE has an …

Weighted qmix: Expanding monotonic value function factorisation for deep multi-agent reinforcement learning

T Rashid, G Farquhar, B Peng… - Advances in neural …, 2020 - proceedings.neurips.cc
QMIX is a popular $ Q $-learning algorithm for cooperative MARL in the centralised training
and decentralised execution paradigm. In order to enable easy decentralisation, QMIX …

Facmac: Factored multi-agent centralised policy gradients

B Peng, T Rashid… - Advances in …, 2021 - proceedings.neurips.cc
Abstract We propose FACtored Multi-Agent Centralised policy gradients (FACMAC), a new
method for cooperative multi-agent reinforcement learning in both discrete and continuous …

Fop: Factorizing optimal joint policy of maximum-entropy multi-agent reinforcement learning

T Zhang, Y Li, C Wang, G Xie… - … conference on machine …, 2021 - proceedings.mlr.press
Value decomposition recently injects vigorous vitality into multi-agent actor-critic methods.
However, existing decomposed actor-critic methods cannot guarantee the convergence of …

Cooperative and competitive multi-agent systems: From optimization to games

J Wang, Y Hong, J Wang, J Xu, Y Tang… - IEEE/CAA Journal of …, 2022 - ieeexplore.ieee.org
Multi-agent systems can solve scientific issues related to complex systems that are difficult or
impossible for a single agent to solve through mutual collaboration and cooperation …

Pac: Assisted value factorization with counterfactual predictions in multi-agent reinforcement learning

H Zhou, T Lan, V Aggarwal - Advances in Neural …, 2022 - proceedings.neurips.cc
Multi-agent reinforcement learning (MARL) has witnessed significant progress with the
development of value function factorization methods. It allows optimizing a joint action-value …

Decentralized multi-agent based energy management of microgrid using reinforcement learning

E Samadi, A Badri, R Ebrahimpour - … Journal of Electrical Power & Energy …, 2020 - Elsevier
This paper proposes a multi-agent based decentralized energy management approach in a
grid-connected microgrid (MG). The MG comprises of wind and photovoltaic resources …

Resource allocation and trajectory design in UAV-aided cellular networks based on multiagent reinforcement learning

S Yin, FR Yu - IEEE Internet of Things Journal, 2021 - ieeexplore.ieee.org
In this article, we focus on a downlink cellular network, where multiple unmanned aerial
vehicles (UAVs) serve as aerial base stations for ground users through frequency-division …