Path planning and obstacle avoidance are two challenging problems in the study of intelligent robots. In this paper, we develop a new method to alleviate these problems based on deep Q-learning with experience replay and heuristic knowledge. In this method, a neural network has been used to resolve the “ curse of dimensionality ” issue of the Q-table in reinforcement learning. When a robot is walking in an unknown environment, it collects experience data which is used for training a neural network ; such a process is called experience replay. Heuristic knowledge helps the robot avoid blind exploration and provides more effective data for training the neural network. The simulation results show that in comparison with the existing methods, our method can converge to an optimal action strategy with less time and can explore a path in an unknown environment with fewer steps and larger average reward.

中文翻译：

---

基于具有经验重播和启发式知识的深度Q学习的智能机器人路径规划

路径规划和避障是智能机器人研究中的两个难题。在本文中，我们基于具有经验重播和启发式知识的深度Q学习，开发了一种缓解这些问题的新方法。在这种方法中，神经网络已被用于解决强化学习中Q表的“维数诅咒”问题。当机器人在未知环境中行走时，它会收集用于训练神经网络的经验数据；这样的过程称为体验重播。启发式知识可帮助机器人避免盲目探索，并为训练神经网络提供更有效的数据。仿真结果表明，与现有方法相比，