A comprehensive survey of multiagent reinforcement learning
Multiagent systems are rapidly finding applications in a variety of domains, including
robotics, distributed control, telecommunications, and economics. The complexity of many …
robotics, distributed control, telecommunications, and economics. The complexity of many …
Multi-agent reinforcement learning: An overview
Multi-agent systems can be used to address problems in a variety of domains, including
robotics, distributed control, telecommunications, and economics. The complexity of many …
robotics, distributed control, telecommunications, and economics. The complexity of many …
Actor-attention-critic for multi-agent reinforcement learning
Reinforcement learning in multi-agent scenarios is important for real-world applications but
presents challenges beyond those seen in single-agent settings. We present an actor-critic …
presents challenges beyond those seen in single-agent settings. We present an actor-critic …
Multi-agent reinforcement learning for network selection and resource allocation in heterogeneous multi-RAT networks
The rapid production of mobile devices along with the wireless applications boom is
continuing to evolve daily. This motivates the exploitation of wireless spectrum using …
continuing to evolve daily. This motivates the exploitation of wireless spectrum using …
Multi-agent reinforcement learning: A survey
Multi-agent systems are rapidly finding applications in a variety of domains, including
robotics, distributed control, telecommunications, economics. Many tasks arising in these …
robotics, distributed control, telecommunications, economics. Many tasks arising in these …
Explaining black box drug target prediction through model agnostic counterfactual samples
Many high-performance DTA deep learning models have been proposed, but they are
mostly black-box and thus lack human interpretability. Explainable AI (XAI) can make DTA …
mostly black-box and thus lack human interpretability. Explainable AI (XAI) can make DTA …
Learning-based physical layer communications for multiagent collaboration
Consider a collaborative task carried out by two autonomous agents that can communicate
over a noisy channel. Each agent is only aware of its own state, while the accomplishment of …
over a noisy channel. Each agent is only aware of its own state, while the accomplishment of …
Towards multi-agent reinforcement learning for integrated network of optimal traffic controllers (MARLIN-OTC)
Traffic congestion can be alleviated by infrastructure expansions; however, improving the
existing infrastructure using traffic control is more plausible due to the obvious financial …
existing infrastructure using traffic control is more plausible due to the obvious financial …
Counterfactual explanation with multi-agent reinforcement learning for drug target prediction
Motivation: Many high-performance DTA models have been proposed, but they are mostly
black-box and thus lack human interpretability. Explainable AI (XAI) can make DTA models …
black-box and thus lack human interpretability. Explainable AI (XAI) can make DTA models …
Automata guided semi-decentralized multi-agent reinforcement learning
This paper investigates the problem of deploying a multi-robot team to satisfy a syntactically
co-safe Truncated Linear Temporal Logic (scTLTL) task specification via multi-agent …
co-safe Truncated Linear Temporal Logic (scTLTL) task specification via multi-agent …