论文标题
q学习与在线随机森林
Q-learning with online random forests
论文作者
论文摘要
$ Q $ - 学习是最基本的无模型增强算法。 $ q $ - 学习的部署需要近似状态行动值函数（也称为$ q $函数）。在这项工作中，我们将在线随机森林提供为$ Q $功能近似器，并提出了一种新颖的方法，其中随机森林随着学习的发展而种植（通过扩大森林）。我们证明了在两个Openai体育馆（“二十一点”和``''''''''Lunar Lander'体育馆中，我们的方法的性能提高了我们的方法的性能。我们怀疑随机森林所享受的过度适应的弹性建议我们对不需要强烈代表问题领域的常见任务方法。我们表明，不断扩大的森林（随着数据的出现，树木数量增加）提高了绩效，这表明扩大的森林对于超越强化学习环境的其他在线随机森林的应用可行。
$Q$-learning is the most fundamental model-free reinforcement learning algorithm. Deployment of $Q$-learning requires approximation of the state-action value function (also known as the $Q$-function). In this work, we provide online random forests as $Q$-function approximators and propose a novel method wherein the random forest is grown as learning proceeds (through expanding forests). We demonstrate improved performance of our methods over state-of-the-art Deep $Q$-Networks in two OpenAI gyms (`blackjack' and `inverted pendulum') but not in the `lunar lander' gym. We suspect that the resilience to overfitting enjoyed by random forests recommends our method for common tasks that do not require a strong representation of the problem domain. We show that expanding forests (in which the number of trees increases as data comes in) improve performance, suggesting that expanding forests are viable for other applications of online random forests beyond the reinforcement learning setting.
