论文标题
用于加固学习任务的遗传算法中的直接突变和交叉
Direct Mutation and Crossover in Genetic Algorithms Applied to Reinforcement Learning Tasks
论文作者
论文摘要
最近，Neuroo进化在增强学习（RL）设置方面非常有竞争力，并能够减轻基于梯度的方法的一些缺点。本文将着重于使用简单的遗传算法（GA）应用神经进化，以找到产生最佳行为能力的神经网络的权重。此外，我们提出了两种新颖的修改，与初始实施相比，提高了数据效率和收敛速度。对OpenAI Gym提供的冰冻环境进行了评估，并被证明比基线方法要好得多。
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.
