A survey on reinforcement learning methods in character animation
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 …
trained to make sequential decisions, and achieve a particular goal within an arbitrary …
A review of cooperation in multi-agent learning
Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous
disciplines, including game theory, economics, social sciences, and evolutionary biology …
disciplines, including game theory, economics, social sciences, and evolutionary biology …
Qplex: Duplex dueling multi-agent q-learning
We explore value-based multi-agent reinforcement learning (MARL) in the popular
paradigm of centralized training with decentralized execution (CTDE). CTDE has an …
paradigm of centralized training with decentralized execution (CTDE). CTDE has an …
Weighted qmix: Expanding monotonic value function factorisation for deep multi-agent reinforcement learning
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 …
and decentralised execution paradigm. In order to enable easy decentralisation, QMIX …
Facmac: Factored multi-agent centralised policy gradients
Abstract We propose FACtored Multi-Agent Centralised policy gradients (FACMAC), a new
method for cooperative multi-agent reinforcement learning in both discrete and continuous …
method for cooperative multi-agent reinforcement learning in both discrete and continuous …
Fop: Factorizing optimal joint policy of maximum-entropy multi-agent reinforcement learning
Value decomposition recently injects vigorous vitality into multi-agent actor-critic methods.
However, existing decomposed actor-critic methods cannot guarantee the convergence of …
However, existing decomposed actor-critic methods cannot guarantee the convergence of …
Cooperative and competitive multi-agent systems: From optimization to games
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 …
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 …
development of value function factorization methods. It allows optimizing a joint action-value …
Decentralized multi-agent based energy management of microgrid using reinforcement learning
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 …
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
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 …
vehicles (UAVs) serve as aerial base stations for ground users through frequency-division …