Department of Electronic Communication, Batman University, Batman 72500, Turkey.
Electrical and Electronics Engineering Department, Dicle University, Diyarbakir 21280, Turkey.
Comput Intell Neurosci. 2022 Sep 5;2022:4115767. doi: 10.1155/2022/4115767. eCollection 2022.
Advances in deep learning significantly affect reinforcement learning, which results in the emergence of Deep RL (DRL). DRL does not need a data set and has the potential beyond the performance of human experts, resulting in significant developments in the field of artificial intelligence. However, because a DRL agent has to interact with the environment a lot while it is trained, it is difficult to be trained directly in the real environment due to the long training time, high cost, and possible material damage. Therefore, most or all of the training of DRL agents for real-world applications is conducted in virtual environments. This study focused on the difficulty in a mobile robot to reach its target by making a path plan in a real-world environment. The Minimalistic Gridworld virtual environment has been used for training the DRL agent, and to our knowledge, we have implemented the first real-world implementation for this environment. A DRL algorithm with higher performance than the classical Deep Q-network algorithm was created with the expanded environment. A mobile robot was designed for use in a real-world application. To match the virtual environment with the real environment, algorithms that can detect the position of the mobile robot and the target, as well as the rotation of the mobile robot, were created. As a result, a DRL-based mobile robot was developed that uses only the top view of the environment and can reach its target regardless of its initial position and rotation.
深度学习的进展极大地影响了强化学习，从而产生了深度强化学习（DRL）。DRL 不需要数据集，并且具有超越人类专家表现的潜力，从而在人工智能领域取得了重大进展。然而，由于 DRL 代理在训练过程中必须与环境进行大量交互，因此由于训练时间长、成本高和可能的材料损坏，直接在真实环境中进行训练是困难的。因此，对于现实世界应用的 DRL 代理的大部分或全部训练都是在虚拟环境中进行的。本研究侧重于移动机器人在真实环境中通过制定路径计划到达目标时所面临的困难。最小网格世界虚拟环境已被用于训练 DRL 代理，据我们所知，我们已经为该环境实现了第一个真实世界的实现。使用扩展环境创建了一种性能优于经典深度 Q 网络算法的 DRL 算法。设计了一种移动机器人用于现实世界的应用。为了使虚拟环境与真实环境相匹配，创建了可以检测移动机器人和目标的位置以及移动机器人的旋转的算法。结果，开发了一种基于 DRL 的移动机器人，它仅使用环境的俯视图，并且可以到达其目标，而不管其初始位置和旋转如何。
