Direct Mutation and Crossover in Genetic Algorithms Applied to Reinforcement Learning Tasks

• URL: http://arxiv.org/abs/2201.04815v1
• Date: Thu, 13 Jan 2022 07:19:28 GMT
• Title: Direct Mutation and Crossover in Genetic Algorithms Applied to Reinforcement Learning Tasks
• Authors: Tarek Faycal and Claudio Zito
• Abstract summary: This paper will focus on applying neuroevolution using a simple genetic algorithm (GA) to find the weights of a neural network that produce optimally behaving agents. We present two novel modifications that improve the data efficiency and speed of convergence when compared to the initial implementation.
• Score: 0.9137554315375919
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neuroevolution has recently been shown to be quite competitive in reinforcement learning (RL) settings, and is able to alleviate some of the drawbacks of gradient-based approaches. This paper will focus on applying neuroevolution using a simple genetic algorithm (GA) to find the weights of a neural network that produce optimally behaving agents. In addition, we present two novel modifications that improve the data efficiency and speed of convergence when compared to the initial implementation. The modifications are evaluated on the FrozenLake environment provided by OpenAI gym and prove to be significantly better than the baseline approach.

Related papers

• MARS: Unleashing the Power of Variance Reduction for Training Large Models [56.47014540413659]
  Large gradient algorithms like Adam, Adam, and their variants have been central to the development of this type of training. We propose a framework that reconciles preconditioned gradient optimization methods with variance reduction via a scaled momentum technique.
  arXiv  Detail & Related papers  (2024-11-15T18:57:39Z)
• Optimal feature rescaling in machine learning based on neural networks [0.0]
  An optimal rescaling of input features (OFR) is carried out by a Genetic Algorithm (GA) The OFR reshapes the input space improving the conditioning of the gradient-based algorithm used for the training. The approach has been tested on a FFNN modeling the outcome of a real industrial process.
  arXiv  Detail & Related papers  (2024-02-13T21:57:31Z)
• Genetic Algorithm enhanced by Deep Reinforcement Learning in parent selection mechanism and mutation : Minimizing makespan in permutation flow shop scheduling problems [0.18846515534317265]
  The proposed RL+GA method was specifically tested on the flow shop scheduling problem (FSP) The hybrid algorithm incorporates neural networks (NN) and uses the off-policy method Q-learning. Results of the study highlight the effectiveness of the RL+GA approach in improving the performance of the primitive GA.
  arXiv  Detail & Related papers  (2023-11-10T08:51:42Z)
• Stochastic Unrolled Federated Learning [85.6993263983062]
  We introduce UnRolled Federated learning (SURF), a method that expands algorithm unrolling to federated learning. Our proposed method tackles two challenges of this expansion, namely the need to feed whole datasets to the unrolleds and the decentralized nature of federated learning.
  arXiv  Detail & Related papers  (2023-05-24T17:26:22Z)
• Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks [51.92362217307946]
  Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems. PINNs are trapped in training failures when the target functions to be approximated exhibit high-frequency or multi-scale features. In this paper, we propose to employ implicit gradient descent (ISGD) method to train PINNs for improving the stability of training process.
  arXiv  Detail & Related papers  (2023-03-03T08:17:47Z)
• Low-Variance Forward Gradients using Direct Feedback Alignment and Momentum [0.0]
  We propose an algorithm that combines Activity-Perturbed Forward Gradients with Direct Feedback Alignment and momentum. Our approach enables faster convergence and better performance when compared to other local alternatives to backpropagation.
  arXiv  Detail & Related papers  (2022-12-14T15:30:56Z)
• A comparative study of back propagation and its alternatives on multilayer perceptrons [0.0]
  The de facto algorithm for training the back pass of a feedforward neural network is backpropagation (BP) The use of almost-everywhere differentiable activation functions made it efficient and effective to propagate the gradient backwards through layers of deep neural networks. In this paper, we analyze the stability and similarity of predictions and neurons in convolutional neural networks (CNNs) and propose a new variation of one of the algorithms.
  arXiv  Detail & Related papers  (2022-05-31T18:44:13Z)
• Cogradient Descent for Dependable Learning [64.02052988844301]
  We propose a dependable learning based on Cogradient Descent (CoGD) algorithm to address the bilinear optimization problem. CoGD is introduced to solve bilinear problems when one variable is with sparsity constraint. It can also be used to decompose the association of features and weights, which further generalizes our method to better train convolutional neural networks (CNNs)
  arXiv  Detail & Related papers  (2021-06-20T04:28:20Z)
• Adam revisited: a weighted past gradients perspective [57.54752290924522]
  We propose a novel adaptive method weighted adaptive algorithm (WADA) to tackle the non-convergence issues. We prove that WADA can achieve a weighted data-dependent regret bound, which could be better than the original regret bound of ADAGRAD.
  arXiv  Detail & Related papers  (2021-01-01T14:01:52Z)
• Adaptive Gradient Method with Resilience and Momentum [120.83046824742455]
  We propose an Adaptive Gradient Method with Resilience and Momentum (AdaRem) AdaRem adjusts the parameter-wise learning rate according to whether the direction of one parameter changes in the past is aligned with the direction of the current gradient. Our method outperforms previous adaptive learning rate-based algorithms in terms of the training speed and the test error.
  arXiv  Detail & Related papers  (2020-10-21T14:49:00Z)
• Semi-Implicit Back Propagation [1.5533842336139065]
  We propose a semi-implicit back propagation method for neural network training. The difference on the neurons are propagated in a backward fashion and the parameters are updated with proximal mapping. Experiments on both MNIST and CIFAR-10 demonstrate that the proposed algorithm leads to better performance in terms of both loss decreasing and training/validation accuracy.
  arXiv  Detail & Related papers  (2020-02-10T03:26:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.