论文标题
在语义环境中绘制神经网络和强化学习
Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments
论文作者
论文摘要
行为产生中使用的大多数强化学习方法都利用媒介信息作为输入。但是，这要求网络具有预定层的输入大小 - 在语义环境中，这意味着假设车辆数量最多。此外，此矢量表示并不是车辆命令和数量不变。为了减轻上述缺点，我们建议将图形神经网络与参与者批评的增强学习相结合。由于图形神经网络将同一网络应用于每辆车辆并汇总传入的边缘信息，因此它们是车辆的数量和顺序不变的。这使其成为在语义环境中用作网络的理想候选者 - 由对象列表组成的环境。图神经网络具有其他一些优势，使它们有利于在语义环境中使用。关系信息是明确给出的，不必推断。此外，图形神经网络通过网络传播信息，并可以收集更高的信息。我们使用高速公路车道变化方案演示了我们的方法，并将图形神经网络的性能与传统的方案进行比较。我们表明，图形神经网络能够在训练和应用过程中处理具有不同数量和车辆级数的场景。
Most reinforcement learning approaches used in behavior generation utilize vectorial information as input. However, this requires the network to have a pre-defined input-size -- in semantic environments this means assuming the maximum number of vehicles. Additionally, this vectorial representation is not invariant to the order and number of vehicles. To mitigate the above-stated disadvantages, we propose combining graph neural networks with actor-critic reinforcement learning. As graph neural networks apply the same network to every vehicle and aggregate incoming edge information, they are invariant to the number and order of vehicles. This makes them ideal candidates to be used as networks in semantic environments -- environments consisting of objects lists. Graph neural networks exhibit some other advantages that make them favorable to be used in semantic environments. The relational information is explicitly given and does not have to be inferred. Moreover, graph neural networks propagate information through the network and can gather higher-degree information. We demonstrate our approach using a highway lane-change scenario and compare the performance of graph neural networks to conventional ones. We show that graph neural networks are capable of handling scenarios with a varying number and order of vehicles during training and application.
