Search Results for author:
Found 5 papers, 0 papers with code
Maximizing Ensemble Diversity in Deep Reinforcement Learning
In this paper, we describe Maximize Ensemble Diversity in Reinforcement Learning (MED-RL), a set of regularization methods inspired from the economics and consensus optimization to improve diversity in the ensemble-based deep reinforcement learning methods by encouraging inequality between the networks during training.
Asymptotic Optimality of Self-Representative Low-Rank Approximation and Its Applications
The approach is based on the concept of {\em self-rank}, defined as the minimum number of samples needed to reconstruct all samples with an accuracy proportional to the rank-$K$ approximation.
Preventing Value Function Collapse in Ensemble Q-Learning by Maximizing Representation Diversity
Recently, the Maxmin and Ensemble Q-learning algorithms used the different estimates provided by ensembles of learners to reduce the bias.
Internet of Things Applications: Animal Monitoring with Unmanned Aerial Vehicle
To achieve the information efficiently, we propose a path planning approach for the UAV based on a Markov decision process (MDP) model.
Agent-based modeling of a price information trading business
Our simulation uses real world geographic data, lifestyle-dependent driving patterns and vehicle models to create an agent-based model of the drivers.
