اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدEmpowerment -- an Introduction
101
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
This book chapter is an introduction to and an overview of the information-theoretic, task independent utility function Empowerment, which is defined as the channel capacity between an agents actions and an agents sensors. It quantifies how much influence and control an agent has over the world it can perceive. This book chapter discusses the general idea behind empowerment as an intrinsic motivation and showcases several previous applications of empowerment to demonstrate how empowerment can be applied to different sensor-motor configuration, and how the same formalism can lead to different observed behaviors. Furthermore, we also present a fast approximation for empowerment in the continuous domain.
قيم البحث
اقرأ أيضاً
One difficulty in using artificial agents for human-assistive applications lies in the challenge of accurately assisting with a persons goal(s). Existing methods tend to rely on inferring the humans goal, which is challenging when there are many pote
ntial goals or when the set of candidate goals is difficult to identify. We propose a new paradigm for assistance by instead increasing the humans ability to control their environment, and formalize this approach by augmenting reinforcement learning with human empowerment. This task-agnostic objective preserves the persons autonomy and ability to achieve any eventual state. We test our approach against assistance based on goal inference, highlighting scenarios where our method overcomes failure modes stemming from goal ambiguity or misspecification. As existing methods for estimating empowerment in continuous domains are computationally hard, precluding its use in real time learned assistance, we also propose an efficient empowerment-inspired proxy metric. Using this, we are able to successfully demonstrate our method in a shared autonomy user study for a challenging simulated teleoperation task with human-in-the-loop training.
The management and combination of uncertain, imprecise, fuzzy and even paradoxical or high conflicting sources of information has always been, and still remains today, of primal importance for the development of reliable modern information systems in
volving artificial reasoning. In this introduction, we present a survey of our recent theory of plausible and paradoxical reasoning, known as Dezert-Smarandache Theory (DSmT), developed for dealing with imprecise, uncertain and conflicting sources of information. We focus our presentation on the foundations of DSmT and on its most important rules of combination, rather than on browsing specific applications of DSmT available in literature. Several simple examples are given throughout this presentation to show the efficiency and the generality of this new approach.
StarCraft II (SC2) is a real-time strategy game, in which players produce and control multiple units to win. Due to its difficulties, such as huge state space, various action space, a long time horizon, and imperfect information, SC2 has been a resea
rch highlight in reinforcement learning research. Recently, an SC2 agent called AlphaStar is proposed which shows excellent performance, obtaining a high win-rates of 99.8% against Grandmaster level human players. We implemented a mini-scaled version of it called mini-AlphaStar based on their paper and the pseudocode they provided. The usage and analysis of it are shown in this technical report. The difference between AlphaStar and mini-AlphaStar is that we substituted the hyper-parameters in the former version with much smaller ones for mini-scale training. The codes of mini-AlphaStar are all open-sourced. The objective of mini-AlphaStar is to provide a reproduction of the original AlphaStar and facilitate the future research of RL on large-scale problems.
The restricted Boltzmann machine is a network of stochastic units with undirected interactions between pairs of visible and hidden units. This model was popularized as a building block of deep learning architectures and has continued to play an impor
tant role in applied and theoretical machine learning. Restricted Boltzmann machines carry a rich structure, with connections to geometry, applied algebra, probability, statistics, machine learning, and other areas. The analysis of these models is attractive in its own right and also as a platform to combine and generalize mathematical tools for graphical models with hidden variables. This article gives an introduction to the mathematical analysis of restricted Boltzmann machines, reviews recent results on the geometry of the sets of probability distributions representable by these models, and suggests a few directions for further investigation.
We present an introduction to the theory of algebraic geometry codes. Starting from evaluation codes and codes from order and weight functions, special attention is given to one-point codes and, in particular, to the family of Castle codes.
الأسئلة المقترحة
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
