Information Theory in Multi-Agent Systems: Methods and Applications
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: closed (20 April 2024) | Viewed by 4260
Special Issue Editor
Interests: intelligent robots; decision support systems; artificial intelligence; multi-agent systems; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, research on the topic of the intelligent control and autonomous decision making of Multi-Agent System (MAS) has made a splash in the real-world in the form of autonomous driving, multi-robot collaboration, and MOBA games. Despite the great success of these emerging techniques in many AI tasks, they still suffer from several limitations, such as long horizons, sparse rewards, noisy disruptions, vulnerability to unstable environments, and the "black box" nature of DNNs, which obscures the understanding of their internal representation and decision-making processes.
Innovative approaches such as unsupervised reinforcement learning (URL) have brought about a breakthrough in solving the aforementioned problems. In this area, the contribution of Information Theory could be highly impactful. How to deal with the mutual information objectives, state entropy, and uncertainty evaluations involved in intelligent methods are essential but difficult issues to be addressed. New emergent machine learning technologies (e.g., unsupervised reinforcement learning), information theory (e.g., maximize mutual information), variational approximation, entropy estimators, and so forth will offer us new solutions.
This Special Issue welcomes the submission of new perspectives, theories, algorithms, and applications of multi-agent systems involving information theory on the central issues of efficiency, generalization, robustness, and interpretability.
Prof. Dr. Haobin Shi
Guest Editor
Manuscript Submission Information
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Keywords
- multi-agent reinforcement learning
- unsupervised reinforcement learning
- information theory
- information decomposition
- variational approximation
- latent representation
- maximize mutual information
- causal reinforcement learning
- entropy estimator
- contrastive-like unsupervised learning
- latent-conditioned policy
- uncertainty estimation
- entropy-like intrinsic reward
- meta-reinforcement learning training
- state entropy
- multi-agent datasets with complex interactions and relations
- multi-agent motion planning and decision making
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