"n_step": 1, # Algorithm for good policies "good_policy": "maddpg", # Algorithm for adversary policies "adv_policy": "maddpg", # === Replay buffer === # Size of the replay buffer. Note that if async_updates is set, then # each worker will have a replay buffer of this size. ...
flower pollinationalgorithm 花授粉算法 cuttlefish optimizationalgorithm乌贼优化算法 Nelder–Mead算法1链接:https://blog.csdn.net/qq_39338671/article/details/86987491
Under the standard assumptions on the graph connectivity, local cost functions and game mappings, it is derived that the players’ actions driven by the proposed algorithm with a small constant step-size are able to converge to a small neighborhood of the NE at a geometric rate with the error...
The training curves for the augmented DFA and BP are shown in Fig.2a. For comparison, we also show the results for the BP algorithm in Eq. (1) withf’(a) replaced withg(a). Here, theg(a) was generated from the various nonlinear activations and the random Fourier series with 100 ra...
The classic Kiefer-Wolfowitz algorithm, using stepsize-control, is one such algorithm that estimates a divided difference approximation of the gradient. This article presents a sampling-controlled version of this algorithm that also uses divided difference estimates and has the benefit of being easily ...
randomizedgradient-freeoraclesIn this paper, a distributed randomized gradient-free algorithm (DRGF) is employed to solve the complex non-convex economic dispatch problem whose non-convex constraints include valve-point loading effects, prohibited operating zones, and multiple fuel options. The DRGF ...
Gradient Free Optimization(无梯度优化算法)是一种优化方法,它不需要目标函数可导,适用于离散的不连续或者其他非连续问题。最常用的无梯度优化算法有遗传算法、粒子群算法、模拟退火算法和Nelder- Mead simplex algorithm。 具体来说,遗传算法是基于生物进化原理的一种优化算法,通过模拟基因遗传和突变的过程来搜索最优解...
intensive full-wave electromagnetic simulation that does not compute gradients with respect to the material distribution function. To make metasurface optimization tractable, the material distribution function can be parameterized with a set of design variables that are tuned by an optimization algorithm. ...
In this study, an efficient stochastic gradient-free method, the ensemble neural networks (ENN), is developed. In the ENN, the optimization process relies on covariance matrices rather than derivatives. The covariance matrices are calculated by the ensemble randomized maximum likelihood algorithm (EnR...
Hill climbing algorithm with the addition of increasing epsilon by a factor if no better neighbour was found. Convex FunctionNon-convex Function Simulated Annealing Adds a probability to the hill climbing to move to a worse position in the search-space to escape local optima with decreasing probabi...