Learning Graphical Models
Repairing Neural Networks by Leaving the Right Past Behind Ryutaro T anno
Machine learning (ML) models often exhibit unexpected failures once deployed in the "wild".
FACMAC: Factored Multi-Agent Centralised Policy Gradients Bei Peng University of Liverpool T abish Rashid University of Oxford Christian A. Schroeder de Witt
However, unlike QMIX, there are no inherent constraints on factoring the critic. We thus also employ a nonmonotonic factorisation and empirically demonstrate that its increased representational capacity allows it to solve some tasks that cannot be solved with monolithic, or monotonically factored critics.
Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems
Multi-agent control is a central theme in the Cyber-Physical Systems (CPS) . However, current control methods either receive non-Markovian states due to insufficient sensing and decentralized design, or suffer from poor convergence.
Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall
We study the problem of learning a Nash equilibrium (NE) in an imperfect information game (IIG) through self-play.
Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall
We study the problem of learning a Nash equilibrium (NE) in an imperfect information game (IIG) through self-play.