# Data-Aware Path Planning for Autonomous Vehicles Using Reinforcement Learning +
## 2 Related Work
### Reinforcement Learning  
Machine learning (ML) is a foundational domain within artificial intelligence (AI). It equips computers with the ability to learn and make decisions that mimic human intelligence [25]. This field is particularly renowned for its applications in signal processing and image processing [25; 26; 27], including significant advancements in speech recognition [28; 29], natural language processing [30; 31; 32], and medical diagnostics [33; 34]. At the heart of machine learning are three primary paradigms, namely supervised learning, where models make decisions based on previously provided output labels; unsupervised learning, which identifies patterns without prior output label knowledge; and reinforcement learning (RL), distinguished by its use of sequential actions to learn from interactions with the environment based on rewards or penalties [35]. RL is defined as a method by which an agent interacts with its environment to learn optimal behaviors without prior knowledge [36]. RL aims to maximize numerical rewards or minimize penalties. This interaction is facilitated by feedback on each action, allowing the agent to iteratively refine its policy to achieve the best possible control policy [35]. Figure 1 depicts this RL control loop. At each time step \(t\), an agent follows an algorithm to observe the state of the system \(s_{t}\), takes an action \(a_{t}\), and receives a reward \(r_{t}\). Based on its current policy, the agent's action leads to a new state. With each interaction, the agent updates its understanding of the environment. Figure 1: Reinforcement learning control loop. RL is a versatile and powerful technique with a broad range of applications, including task scheduling, game strategy development, image-based robot control, and path planning [37; 38; 39; 40; 41]. The actor-critic structure [42; 43; 44] and Q-learning [45; 46; 47; 48] are frameworks in which RL can be implemented [36]. This diversity in approaches allows RL to effectively address complex decision-making and optimization problems across different domains. Various network structures, including wireless sensor networks [49], vehicular ad hoc networks (VANETs) [50], flying ad hoc networks [51], and drone ad hoc networks [52], benefit from the application of RL algorithms to their routing algorithms [53]. VANETs present a unique application domain for RL. These networks, which facilitate communication between vehicles and road-side infrastructure, support a wide range of applications, from emergency alerts to road safety and information services [54; 55; 56; 57; 58]. High mobility and computational capabilities are characteristics of VANETs that make them an interesting subject for RL-based optimization [59]. In their work, [PERSON] et al. compared RL-based routing protocols, analyzing their mechanisms, advantages, disadvantages, and practical applications [53]. RL algorithms primarily focus on optimizing various quality-of-service metrics, including throughput, overhead, and end-to-end delay [60; 61]. RL has shown promise within the field of autonomous vehicle motion planning. Solutions for planning challenges have been developed using artificial intelligence, including deep learning models for optimal control action learning and multi-robot navigation algorithms that consider dynamic obstacles [62; 63; 64; 65; 66]. Beyond these applications, the versatility of RL extends to a wide array of driving functionalities such as car following [67], lane keeping [68], driver assistance [69], lane merging [70], highway merging [71], braking [72], and active steering [73]. RL solutions for traffic signal control have also been shown to outperform standard control methods in simulation environments [74]. Each of these applications demonstrates the potential of RL not only to enhance navigational strategies and the driving performance of autonomous vehicles but also to contribute significantly to safety and efficiency in complex traffic scenarios.