在这里明确了梯度下降是“找到使函数值最小的那个自变量X”,换句话说,在常见的的分类问题中,就算找到使损失函数(Loss Function)最小的那个参数 θ 。这是梯度下降,找到全局最小值(当然大概率是局部最小值) 在强化学习中,我们没有确切的损失函数,我们无法让损失最小,代替的目标是最大化奖励函数(Reward Function)...
强化学习使用的是gradient ascent,目的是让reward最大化;而深度学习使用的是gradient descent,目的是让loss最小化 主要功能:解决公式中存在不可微分东西 优化目标:增加reward大的事件集合出现的概率 PG公式推导 对于一次游戏中: 我们定义本次游戏所有的事件集合 τ=s1,a1,r1,⋯,st,at,rt ,其中: st 表示第t...
Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. To find a local minimum of a function using gradient descent, we take steps proportional to the negative of the gradient (or approximate gradient) of the function at the cu...
We then formulate necessary conditions of optimality for this relaxed problem which we leverage to prove convergence of the gradient descent-ascent algorithm to candidate solutions of the original problem. Finally, we showcase the efficiency of our algorithm through numerical simulations involving ...
We also investigate the algorithm without strong convexity and we provide some necessary and sufficient conditions under which the gradient descent-ascent enjoys linear convergence.doi:10.48550/arXiv.2209.01272Zamani, MoslemAbbaszadehpeivasti, Hadi
Gradient Ascent: In the context of machine learning, gradient descent is more common, where we minimize a loss function. However, in gradient ascent, we aim to maximize an objective function. The idea is similar, but the directions are opposite. Your visualization showcases gradient ascent, with...
Method Shuffle scrambles the training data indices contained in the sequence array using the Fisher-Yates algorithm. The heart of gradient descent training is short: XML Copy for (int ti = 0; ti < trainData.Length; ++ti) { int i = sequence[ti]; double computed = ComputeOutput(train...
Method Shuffle scrambles the training data indices contained in the sequence array using the Fisher-Yates algorithm. The heart of gradient descent training is short: XML Copy for (int ti = 0; ti < trainData.Length; ++ti) { int i = sequence[ti]; double computed = ComputeOutput(train...
To find the local minimum using gradient descent, steps proportional to the negative of the gradient of the function at the current point are taken. If taken in the positive direction, the algorithm finds local maximum and this process is called as Gradient Ascent....
4 Gradient-ascent algorithm(REINFORCE) 梯度上升算法最大化目标函数J(\theta): \theta_{t+1} = \theta_t + \alpha \nabla_\theta J(\theta) \\ = \theta_t + \alpha \mathbb E[\nabla_\theta ln \pi(A|S, \theta_t)q_\pi(S,A)] \tag{15}实际通过SGD替代: ...