Efficient and scalable reinforcement learning for large-scale network control
The primary challenge in the development of large-scale artificial intelligence (AI) systems
lies in achieving scalable decision-making—extending the AI models while maintaining …
lies in achieving scalable decision-making—extending the AI models while maintaining …
Proagent: Building proactive cooperative ai with large language models
Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in
the realm of multi-agent systems. Current approaches to develo** cooperative agents rely …
the realm of multi-agent systems. Current approaches to develo** cooperative agents rely …
ProAgent: building proactive cooperative agents with large language models
Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in
the realm of multi-agent systems. Current approaches to develo** cooperative agents rely …
the realm of multi-agent systems. Current approaches to develo** cooperative agents rely …
Taxai: A dynamic economic simulator and benchmark for multi-agent reinforcement learning
Taxation and government spending are crucial tools for governments to promote economic
growth and maintain social equity. However, the difficulty in accurately predicting the …
growth and maintain social equity. However, the difficulty in accurately predicting the …
Optimistic sequential multi-agent reinforcement learning with motivational communication
Abstract Centralized Training with Decentralized Execution (CTDE) is a prevalent paradigm
in the field of fully cooperative Multi-Agent Reinforcement Learning (MARL). Existing …
in the field of fully cooperative Multi-Agent Reinforcement Learning (MARL). Existing …
Byzantine robust cooperative multi-agent reinforcement learning as a bayesian game
In this study, we explore the robustness of cooperative multi-agent reinforcement learning (c-
MARL) against Byzantine failures, where any agent can enact arbitrary, worst-case actions …
MARL) against Byzantine failures, where any agent can enact arbitrary, worst-case actions …
An off-policy multi-agent stochastic policy gradient algorithm for cooperative continuous control
D Guo, L Tang, X Zhang, Y Liang - Neural Networks, 2024 - Elsevier
Multi-agent reinforcement learning (MARL) algorithms based on trust regions (TR) have
achieved significant success in numerous cooperative multi-agent tasks. These algorithms …
achieved significant success in numerous cooperative multi-agent tasks. These algorithms …
Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks
In multi-agent reinforcement learning (MARL), the Centralized Training with Decentralized
Execution (CTDE) framework is pivotal but struggles due to a gap: global state guidance in …
Execution (CTDE) framework is pivotal but struggles due to a gap: global state guidance in …
Enhancing Collaboration in Heterogeneous Multiagent Systems Through Communication Complementary Graph
K Peng, T Ma, L Jia, H Rong - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Heterogeneous multiagent systems are characterized by diverse task distributions, which
are prevalent in practical scenarios, such as distributed decision making and robotic …
are prevalent in practical scenarios, such as distributed decision making and robotic …
FTR‐Bench: Benchmarking Deep Reinforcement Learning for Flipper‐Track Robot Control
Tracked robots equipped with flippers and sensors are extensively employed in outdoor
search and rescue scenarios. However, achieving precise motion control on complex …
search and rescue scenarios. However, achieving precise motion control on complex …