Reinforcement learning has become one of the most trending subjects in the recent decade. It has seen applications in various fields such as robot manipulations, autonomous driving, path planning, computer gaming, etc. We accomplished three tasks during the course of this project. Firstly, we studied the Q-learning algorithm for tabular environments and applied it successfully to an OpenAi Gym environment, Taxi. Secondly, we gained an understanding of and implemented the deep Q-network algorithm for Cart-Pole environment. Thirdly, we also studied the application of reinforcement learning in autonomous driving and its combination with safety check constraints (safety controllers). We trained a rough autonomous driving agent using highway-gym environment and explored the effects of various environment configurations like reward functions on the agent training performance.

翻译：强化学习已成为近十年来最流行的科目之一,在机器人操纵、自主驾驶、路径规划、路径规划、计算机赌博等各个领域都有应用。 在该项目实施过程中,我们完成了三项任务。首先,我们研究了表格环境的Q学习算法,并成功地将其应用于OpenAi Gym环境,出租车。第二,我们了解并实施了Cart-Pole环境的深Q网络算法。第三,我们还研究了在自主驾驶中应用强化学习及其与安全检查限制相结合(安全控制器)的问题。我们用高速公路-气旋环境培训了一个粗糙的自主驾驶剂,并探讨了各种环境配置的影响,如奖励功能对代理培训绩效的影响。