一、Universal Value Function Approximator (UVFA) 强化学习中,值函数 V(s;θ) 的学习是核心,其中 θ 表示线性特征或者神经网络的参数。在大型MDP中,值函数学习已观察已知的状态,泛化到相似但未知的状态。 单一目标G 值函数隐含针对着MDP中某个固定的目标 G ,如到达迷宫出口,游戏通关等。现在我们把值函数
文章提出了一种Universal Value Function Approximator(UVFA), V(s,g;θ) 用来近似值函数。 方法 文章首先在监督学习框架下研究UVFA来产生一些直观的感受,对比了两种架构,端到端训练和两阶段训练。 端到端训练将s和g合并后输入,损失函数采用MSE。两阶段训练法将值函数的ground-truth写成矩阵形式,每一行表示一个...
An agent that has well understood the environment should be able to apply its skills for any given goals, leading to the fundamental problem of learning the Universal Value Function Approximator (UVFA). A UVFA learns to predict the cumulative rewards between all state-goal pairs. However, ...
Figure2showcases the internal code structure of our universal material model subroutine. Our subroutine computes the free energy function\(\psi \), the Cauchy stress tensor\(\varvec{\sigma }\), and the tangent stiffness tensor\({\mathbb {C}}\)with respect to the scalar invariants\({\bar{...
Neural network with unbounded activation functions is universal approximator Appl. Comput. Harmon. Anal., 43 (2) (2017), pp. 233-268 View PDFView articleView in ScopusGoogle Scholar [73] Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz Discovering governing equations from data by spars...
1a to act as a universal approximator of arbitrarily defined input–output transfer functions of the form: \({\mathbf{H}}\left( \omega \right) = \frac{{{\mathbf{s}}_{\mathrm{o}}\left( \omega \right)}}{{{\mathbf{s}}_{\mathrm{i}}\left( \omega \right)}}\). Fig. 1: ...
为一个 universal successor features approximator (USFA)。 在实际操作中,为了定义一个USFA,需要定义关于策略 的表达。本文是将所有的策略都映射到 维的向量空间中,即 . 这样又可以将 USFs 写作 。由于任何一个reward函数都对应一组最优策略,而任何一个策略都能够作为最优策略对应到一个reward函数中,例如 ...
constant- a number representing a quantity assumed to have a fixed value in a specified mathematical context; "the velocity of light is a constant" Based on WordNet 3.0, Farlex clipart collection. © 2003-2012 Princeton University, Farlex Inc. ...
相应地,我们称 \tilde{\psi}(s, a, \pi) \approx \psi(s, a, \pi) 为一个 universal successor features approximator (USFA)。 在实际操作中,为了定义一个USFA,需要定义关于策略 \pi 的表达。本文是将所有的策略都映射到 k 维的向量空间中,即 e: (\mathcal{S} \mapsto \mathcal{A}) \maps...