Bayesian Nonparametric Reward Learning From Demonstration


Learning from demonstration provides an attractive solution to the problem of teaching autonomous systems the way to perform complex tasks. Reward learning from demonstration is a promising methodology of inferring a rich and transferable representation of the demonstrator's intents, however current algorithms suffer from intractability and inefficiency in massive domains because of the idea that the demonstrator is maximizing one reward operate throughout the entire task. This paper takes a completely different perspective by assuming that the reward perform behind an unsegmented demonstration is actually composed of several distinct subtasks chained together. Leveraging this assumption, a Bayesian nonparametric reward-learning framework is presented that infers multiple subgoals and reward functions inside one unsegmented demonstration. The new framework is developed for discrete state areas and conjointly general continuous demonstration domains using Gaussian process reward representations. The algorithm is shown to possess each performance and computational blessings over existing inverse reinforcement learning ways. Experimental results are given in both cases, demonstrating the power to find out difficult maneuvers from demonstration on a quadrotor and a remote-controlled car.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Predicting Hot Events in the Early Period through Bayesian Model for Social Networks ABSTRACT: It is essential for a wide variety of applications, such as information dissemination mining, ad recommendation, and
PROJECT TITLE : Distributed Bayesian Matrix Decomposition for Big Data Mining and Clustering ABSTRACT: Matrix decomposition is one of the essential tools that must be utilized in order to extract useful information from the
PROJECT TITLE : Robust Empirical Bayesian Reconstruction of Distributed Sources for Electromagnetic Brain Imaging ABSTRACT: Electromagnetic brain imaging uses non-invasive recordings of magnetic fields and electric potentials
PROJECT TITLE : Variational Bayesian Blind Color Deconvolution of Histopathological Images ABSTRACT: In most whole-slide histology images, two or more chemical dyes are used. In digital pathology, slide stain separation or colour
PROJECT TITLE : Bayesian Polytrees With Learned Deep Features for Multi-Class Cell Segmentation ABSTRACT: Quantitative cell biology relies heavily on being able to identify different cell compartments, cell types, and the ways

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry