之前有看过hindsight experience replay(HER)论文,其中用到的核心思想来自于这篇Universal Value Function Approximators(通用价值函数近似器),因此准备回过头好好看看这篇文章。 摘要:价值函数是强化学习系统的一个核心组成部分。其主要思想是构建一个单一的函数近似器V(s;θ),使用参数θ估计任何状态s的长期奖励。在本文...
价值函数是强化学习中在与环境交互中的一种知识的表达方式,用来指引Agent完成指定的任务。要想Agent能够完成多种任务,具有一个通用的价值函数是必不可少的,该论文提出了一种基于目标的价值函数(UVFA V (s; g; …
Universal Value Function Approximators 动机 值函数是强化学习中的重要概念,学习到一个好的值函数是强化学习中的核心问题。普通的值函数通常表达了全局目标下的奖励。文章中采用之前工作中提出的Genaral Value Function(GVF),通过引入目标goal,定义了广义值函数 Vg(s) ,表达了局部目标goal的奖励,能更好的利用当前环境...
playing around with universal successor feature approximators (USFAs), universal value function approximators (UVFAs), successor features and generalized policy iteration (SF&GPI) - tomov/MTRL
The review summarizes and compares numerous conceptually different neural networks-based approaches for constitutive modeling including neural networks used as universal function approximators, advanced neural network models and neural network approaches with integrated physical knowledge. The upcoming of these ...
It is widely known that neural networks (NNs) are universal approximators of continuous functions. However, a less known but powerful result is that a NN with a single hidden layer can accurately approximate any nonlinear continuous operator. This univer
2. Universal Value Function Approximators (UVFAs) UVFA(读作“YOU-fah”)是传统值函数拟合器的推广[4]。传统的值函数拟合器可以用 来表示,针对的是一个特定的任务, 表示值函数的参数。一旦任务发生变化,导致奖励函数也发生变化,则已经拟合出来的参数 ...
Since neural networks are universal approximators [72], they can represent the full range of possible functions that could fit the available data. By training multiple iterations of a UDE model and analyzing their trajectories, we can see a range of feasible outcomes for the system with just ...
Fig. 1: Universal flat-optics approximators: general idea. a Problem setup composed of a flat optical surface constituted by resonant nanostructures: b with input si and output so scattered waves. (i) Dependency of the number of resonances on the dimensions of a nanostructure. c Block diagram ...
[1] Universal Value Function Approximators, ICML 2015. [2] Hindsight Experience Replay, NIPS 2017. [3] Multi-task Deep Reinforcement Learning with PopArt, AAAI 2019. [4] Automatic Goal Generation for Reinforcement Learning Agents, ICML 2018. [5] Visual Hindsight Experience Replay, Axriv 2019.编...