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RLPeri: Accelerating Visual Perimetry Test with Reinforcement Learning and Convolutional Feature Extraction
During the test, a patient's gaze is fixed at a specific location while light stimuli of varying intensities are presented in central and peripheral vision.
Learning Individual Policies in Large Multi-agent Systems through Local Variance Minimization
In multi-agent systems with large number of agents, typically the contribution of each agent to the value of other agents is minimal (e. g., aggregation systems such as Uber, Deliveroo).
Value Variance Minimization for Learning Approximate Equilibrium in Aggregation Systems
For effective matching of resources (e. g., taxis, food, bikes, shopping items) to customer demand, aggregation systems have been extremely successful.
Entropy based Independent Learning in Anonymous Multi-Agent Settings
A key characteristic of the domains of interest is that the interactions between individuals are anonymous, i. e., the outcome of an interaction (competing for demand) is dependent only on the number and not on the identity of the agents.
