论文标题
RLSS：顺序场景生成的深入增强学习算法
RLSS: A Deep Reinforcement Learning Algorithm for Sequential Scene Generation
论文作者
论文摘要
我们提出RLSS：用于顺序场景生成的增强学习算法。这是基于使用生成问题的近端政策优化（PPO）算法。特别是，我们考虑如何通过在学习过程中包括贪婪的搜索算法来有效地减少动作空间。我们的实验表明，我们的方法会收敛于相对较大的动作，并学会以预定义的设计目标生成场景。这种方法将对象迭代放置在虚拟场景中。在每个步骤中，网络都会选择要放置的对象并选择导致最大奖励的位置。如果最后的诉讼导致所需的财产，则分配了高奖励，而违反约束的行为则受到惩罚。我们证明了方法的能力，可以通过解决室内计划问题并产生愤怒的小鸟水平来有效地产生合理和多样化的场景。
We present RLSS: a reinforcement learning algorithm for sequential scene generation. This is based on employing the proximal policy optimization (PPO) algorithm for generative problems. In particular, we consider how to effectively reduce the action space by including a greedy search algorithm in the learning process. Our experiments demonstrate that our method converges for a relatively large number of actions and learns to generate scenes with predefined design objectives. This approach is placing objects iteratively in the virtual scene. In each step, the network chooses which objects to place and selects positions which result in maximal reward. A high reward is assigned if the last action resulted in desired properties whereas the violation of constraints is penalized. We demonstrate the capability of our method to generate plausible and diverse scenes efficiently by solving indoor planning problems and generating Angry Birds levels.
