论文标题
谱系进化增强学习
Lineage Evolution Reinforcement Learning
论文作者
论文摘要
我们提出了一个普通的代理人学习系统，在此基础上，我们提出了谱系进化增强学习算法。谱系进化增强学习是一种衍生算法，与一般代理人人口学习系统一致。我们将DQN及其相关变体的代理作为人群中的基本药物，并将遗传算法中的选择，突变和交叉模块添加到增强学习算法中。在代理演化的过程中，我们指的是自然遗传行为的特征，添加谱系因子以确保代理的潜在性能的保留，并在评估代理的性能时全面考虑当前的性能和谱系值。在不更改原始强化学习算法的参数的情况下，谱系进化增强学习可以优化不同的强化学习算法。我们的实验表明，谱系的进化概念可改善Atari 2600游戏中某些游戏中原始强化学习算法的性能。
We propose a general agent population learning system, and on this basis, we propose lineage evolution reinforcement learning algorithm. Lineage evolution reinforcement learning is a kind of derivative algorithm which accords with the general agent population learning system. We take the agents in DQN and its related variants as the basic agents in the population, and add the selection, mutation and crossover modules in the genetic algorithm to the reinforcement learning algorithm. In the process of agent evolution, we refer to the characteristics of natural genetic behavior, add lineage factor to ensure the retention of potential performance of agent, and comprehensively consider the current performance and lineage value when evaluating the performance of agent. Without changing the parameters of the original reinforcement learning algorithm, lineage evolution reinforcement learning can optimize different reinforcement learning algorithms. Our experiments show that the idea of evolution with lineage improves the performance of original reinforcement learning algorithm in some games in Atari 2600.
