论文标题
基于深度强化学习和传统探路算法的多代理导航
Multi-agent navigation based on deep reinforcement learning and traditional pathfinding algorithm
论文作者
论文摘要
我们开发了一个新框架，用于避免碰撞问题。该框架结合了传统的探路算法和增强学习。在我们的方法中，代理商学习是要导航还是采取简单的行动，以通过在每个时间步骤中通过强化学习训练的深度神经网络来避开其伴侣。该框架使代理商可以在抽象的新场景中到达终端点。在我们的实验中，我们使用Unity3D和TensorFlow来为我们的方案构建模型和环境。我们分析结果并修改参数，以针对我们的代理人采取良好的行为策略。在不同情况下，我们的策略可以附加在不同的环境中，尤其是当量表很大的情况下。
We develop a new framework for multi-agent collision avoidance problem. The framework combined traditional pathfinding algorithm and reinforcement learning. In our approach, the agents learn whether to be navigated or to take simple actions to avoid their partners via a deep neural network trained by reinforcement learning at each time step. This framework makes it possible for agents to arrive terminal points in abstract new scenarios. In our experiments, we use Unity3D and Tensorflow to build the model and environment for our scenarios. We analyze the results and modify the parameters to approach a well-behaved strategy for our agents. Our strategy could be attached in different environments under different cases, especially when the scale is large.
