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Found 7 papers, 1 papers with code
Markov $α$-Potential Games
In this new framework, Markov games are shown to be Markov $\alpha$-potential games, and the existence of an associated $\alpha$-potential function is established.
Pursuit of a Discriminative Representation for Multiple Subspaces via Sequential Games
We consider the problem of learning discriminative representations for data in a high-dimensional space with distribution supported on or around multiple low-dimensional linear subspaces.
Independent and Decentralized Learning in Markov Potential Games
We propose a multi-agent reinforcement learning dynamics, and analyze its convergence in infinite-horizon discounted Markov potential games.
Interpretable Machine Learning Models for Modal Split Prediction in Transportation Systems
Modal split prediction in transportation networks has the potential to support network operators in managing traffic congestion and improving transit service reliability.
BIG-bench Machine Learning
Interpretable Machine Learning
+1
Dynamic Tolling for Inducing Socially Optimal Traffic Loads
How to design tolls that induce socially optimal traffic loads with dynamically arriving travelers who make selfish routing decisions?
Multi-agent Bayesian Learning with Adaptive Strategies: Convergence and Stability
Any fixed point belief consistently estimates the payoff distribution given the fixed point strategy profile.
Bayesian Learning with Adaptive Load Allocation Strategies
We study a Bayesian learning dynamics induced by agents who repeatedly allocate loads on a set of resources based on their belief of an unknown parameter that affects the cost distributions of resources.
