Only in the previous chapter, toward the end, did I talk about Q-learning in a continuous state space. You discretized the state values using an arbitrary approach and trained a learning model. This chapter ext
Approximation theory involves two types of problems. One arises when a function is given explicitly, but one wishes to find other types of functions which are more convenient to work with. The other problem concerns fitting functions to given data and finding the “best” function in a certain...
文献笔记:Issues in Using Function Approximation for Reinforcement Learning 该篇论文描述了采用函数逼近法进行深度强化学习所遇到的问题,即会产生过高估计。 所谓函数逼近,指的是采用复杂函数估计state-value function值。一般Q-learning有以下表示: Q(s,a)−ras+γmax^aaccionQ(s′,^a)Q(s,a)−rsa+γmaxa...
比如在函数拟合(function approximation)中,目标就是让0E_{in}=0,使得原所有样本都尽可能地落在拟合的函数曲线上。 为了避免发生过拟合,我们可以引入正则项\lambda,得到\beta的最优解为: \beta=(Z^TZ+\lambda I)^{-1}Z^Ty 我们再来看一下Z矩阵,Z矩阵是由一系列Gaussian函数组成,每个Gaussian函数计算的是两...
4 DeepQ-learning(DQN) 将神经网络与 Q-learning相结合,使用神经网络来拟合 action value(Q-learning with function approximation 可以使用简单的线性函数来拟合 action value)。 objective function gradient-descent 由于待优化参数w不仅出现在\hat q(S,A,w)中,也出现在y=R + \gamma max_{a\in A(S^{'}...
当然,这种方法在某些领域还是很有用的。比如在函数拟合(function approximation)中,目标就是让 ,使得原所有样本都尽可能地落在拟合的函数曲线上。 为了避免发生过拟合,我们可以引入正则项 ,得到 的最优解为: 我们再来看一下 Z 矩阵,Z 矩阵是由一系列 Gaussian 函数组成,每个 Gaussian 函数计算的是两个样本之间的...
Deep Learning, 2016. Articles Function approximation, Wikipedia. Summary In this tutorial, you discovered the intuition behind neural networks as function approximation algorithms. Specifically, you learned: Training a neural network on data approximates the unknown underlying mapping function from inputs ...
In off-policy control methods, the vulnerability of instability and divergence increases whenever we face a deadly triad which is the combination of off-policy training, bootstrapping and function approximation. With linear function approximators, online TD learning gives some convergence guarantee (...
Incremental extreme learning machine ( x , a i , b i ) can work as universal approximators with adjustable hidden node parameters, from a function approximation point of view the hidden... Lei. Chen - 《Neurocomputing》 被引量: 1082发表: 2007年 Approximation and Estimation Bounds for Artifici...
本文介绍的 Policy Gradient 也叫 Policy Function Approximation,是从之前介绍的 value-based methods 到policy-based methods的转变(即从 value function approximation 到 policy function approximation 的转变)。 之前的 policy 都是通过 table 的形式表达: ...