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),这个时候我们如果想要让奖励函数更大,就要找到使奖励函数更大的那个网络参数 θ ,这时候我们的目标是局部最大值,也只能通过梯度上升(gradient ascent)来找到了。
Gradient ascent works in the same manner as gradient descent, with one difference. The task it fulfills isn’t minimization, but rather maximization of some function. The reason for the difference is that, at times, we may want to reach the maximum, not the minimum of some function; this...
2. Directional Derivative 3. Gradient Descent (opposite = Ascent) https://www.khanacademy.org/math/multivariable-calculus/multivariable-derivatives/gradient-and-directional-derivatives/v/why-the-gradient-is-the-direction-of-steepest-ascent Deeplearning with Gradient Descent: AIGradient Descent Why...
To further reduce the sample complexity, we propose an accelerated Riemannian stochastic gradient descent ascent (Acc-RSGDA) algorithm based on the momentum-based variance-reduced technique. We prove that our Acc-RSGDA algorithm achieves a lower sample complexity of $ilde{O}(\\\kappa ^{4}\\\...
for which the relevant solution concept isStackelberg equilibrium, a generalization of Nash. One of the mostpopular algorithms for solving min-max games is gradient descentascent (GDA). We present a straightforward generalization of GDAto min-max Stackelberg games with dependent strategy sets, butshow...
3.1.4: Loss Function and OptimizationIn policy gradient methods, the loss function is derived from the objective function and usually involves the log-likelihood of actions weighted by rewards or advantages. The policy network is then optimized using gradient ascent or descent on this loss. Here’...
One way to determine the values of \theta that maximizes this function is gradient ascent. This algorithm is closely related to gradient descent, where the differences are: gradient descent is designed to find the minimum of a function, whereas the gradient ascent will find the maximum, and ...
Because the action space is continuous, and we assume the Q-function is differentiable with respect to action, we can just perform gradient ascent (with respect to policy parameters only) to solve Note that the Q-function parameters are treated as constants here. Exploration vs. Exploitation ...
2. Gradient Descent 2.1. A Gradual Decrease In our article on the Java implementation of gradient descent, we studied how this algorithm helps us find the optimal parameters in a machine learning model. We also discussed how gradient descent, or its cousin gradient ascent, can iteratively approxi...
The meaning of GRADIENT is the rate of regular or graded ascent or descent : inclination. How to use gradient in a sentence. Did you know?