6 papers · 1 filter
Learning from Local Walks on Dynamic Graphs with Bandit Feedback
Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni +1
We study stochastic multi-armed bandits on dynamic graphs, where arms correspond to the vertices of a network with time-varying edges. In this setting, the learner is restricted to…
Flickering Multi-Armed Bandits
Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni +1
We introduce Flickering Multi-Armed Bandits (FMAB) to model sequential decision-making in environments with changing action availability, where accessibility of the next action is…
A Unified Framework for Locality in Scalable MARL
Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni +1
Scalable methods for networked multi-agent reinforcement learning let each agent plan using only a small neighborhood of the agent graph. This works only when the system is value-l…
Multi-Agent Lipschitz Bandits
Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni +1
We study the decentralized multi-player stochastic bandit problem over a continuous, Lipschitz-structured action space where hard collisions yield zero reward. Our objective is to…
Incentivized Lipschitz Bandits
Sourav Chakraborty, Amit Kiran Rege, Claire Monteleoni +1
We study incentivized exploration in multi-armed bandit (MAB) settings with infinitely many arms modeled as elements in continuous metric spaces. Unlike classical bandit models, we…
Incentivized Exploration of Non-Stationary Stochastic Bandits
Sourav Chakraborty, Lijun Chen
We study incentivized exploration for the multi-armed bandit (MAB) problem with non-stationary reward distributions, where players receive compensation for exploring arms other tha…