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Found 13 papers, 1 papers with code
Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?
We contribute to micro-data model-based reinforcement learning (MBRL) by rigorously comparing popular generative models using a fixed (random shooting) control agent.
Mass Volume Curves and Anomaly Ranking
Here we formulate the issue of scoring anomalies as a M-estimation problem by means of a novel functional performance criterion, referred to as the Mass Volume curve (MV curve in short), whose optimal elements are strictly increasing transforms of the density almost everywhere on the support of the density.
Calibration of One-Class SVM for MV set estimation
Our approach makes it possible to tune the hyperparameters automatically and obtain nested set estimates.
Parallel Contextual Bandits in Wireless Handover Optimization
As cellular networks become denser, a scalable and dynamic tuning of wireless base station parameters can only be achieved through automated optimization.
Best Arm Identification in Graphical Bilinear Bandits
We study the best arm identification problem in which the learner wants to find the graph allocation maximizing the sum of the bilinear rewards.
Refined bounds for randomized experimental design
Experimental design is an approach for selecting samples among a given set so as to obtain the best estimator for a given criterion.
The guide and the explorer: smart agents for resource-limited iterated batch reinforcement learning
Iterated batch reinforcement learning (RL) is a growing subfield fueled by the demand from systems engineers for intelligent control solutions that they can apply within their technical and organizational constraints.
An $α$-No-Regret Algorithm For Graphical Bilinear Bandits
We propose the first regret-based approach to the Graphical Bilinear Bandits problem, where $n$ agents in a graph play a stochastic bilinear bandit game with each of their neighbors.
Guided Safe Shooting: model based reinforcement learning with safety constraints
In the last decade, reinforcement learning successfully solved complex control tasks and decision-making problems, like the Go board game.
Multi-timestep models for Model-based Reinforcement Learning
In model-based reinforcement learning (MBRL), most algorithms rely on simulating trajectories from one-step dynamics models learned on data.
Model-based Reinforcement Learning
reinforcement-learning
+1
A Multi-step Loss Function for Robust Learning of the Dynamics in Model-based Reinforcement Learning
In model-based reinforcement learning, most algorithms rely on simulating trajectories from one-step models of the dynamics learned on data.
Deep autoregressive density nets vs neural ensembles for model-based offline reinforcement learning
We consider the problem of offline reinforcement learning where only a set of system transitions is made available for policy optimization.
Differentially Private Model-Based Offline Reinforcement Learning
We address offline reinforcement learning with privacy guarantees, where the goal is to train a policy that is differentially private with respect to individual trajectories in the dataset.
