# RL-Based Sim2 Real Enhancements for Autonomous Beach-Cleaning Agents
## 2 State of Art  
The issue of beach cleaning represents a significant environmental challenge, where traditional solutions have demonstrated clear limitations [7; 8]. Despite the prevalence of conventional methods such as the use of shovels and tractorized systems, their effectiveness is compromised in the face of increasingly complex challenges, such as escalating pollution and the urgent need to adopt more sustainable and efficient approaches [9]. The application of autonomous robotics in beach cleaning represents a developing area of research, still in its early stages but showing significant potential for advancement [10; 11]. Traditional beach-cleaning robots, which are predominantly large-scale [12], often require human supervision and/or control, limiting their autonomy and operational efficiency [13; 14; 15]. However, recent advancements in DRL and simulation technologies have introduced new opportunities for improving these systems. Studies employing DRL agents, such as DDPG and DQN, have been conducted solely in simulation [16], demonstrating DRL's capability to train agents that can navigate and operate in complex [17; 18] and dynamic [6; 19] environments, typical of coastal settings. The success of DRL in autonomous vehicles [20; 21] and drones [22; 23; 24] highlights its potential for enhancing autonomous navigation and real-time decision-making, which are crucial for efficient beach cleaning. Integrating Imitation Learning (IL) with RL could offer a robust strategy, where specific routes and cleaning tasks are predefined [25]. Simulating to real-world transfer (Sim2 Real) has become a cornerstone in robotics, proving invaluable for training agents in simulated settings before their real-world application. This method's efficacy in translating simulated learning to practical scenarios is comprehensively analyzed in references [26; 27], with specific applications to mobile robotics discussed in [28; 29]. Extending beyond traditional robotics, Sim2 Real has also facilitated advancements in diverse areas. For instance, deep reinforcement learning has been applied to optimize wind turbine energy output, demonstrating the versatility of Sim2 Real techniques in energy sectors, as explored in [30]. Similarly, convolutional proximal policy optimization has been utilized for mapless navigation [31] and deep deterministic policy gradient methods have been employed for precise target tracking in [32]. In comparison to traditional AI or robotics control techniques, Sim2 Real offers a distinctive advantage, especially in handling unpredictable and dynamic environments like beaches. Traditional methods often struggle to account for the complexities inherent in coastal landscapes, lacking the capability to incorporate factors such as variability in weather conditions, the intermittent presence of objects, and variations in terrain characteristics. Simulations also provides a safe and controllable space to test and refine algorithms before implementation in the field, as discussed in [33; 34; 35], where efforts are made to incorporate safety factors within simulated environments and the policies that the agents learn. The Sim2 Real technique presents significant challenges in robotics and other fields where real-world training is impractical or risky. This process involves training an RL model in a simulated environment and then deploying it in a real-world setting. However, this transition is not without complexities. One major hurdle is the reality gap [36], which refers to the substantial differences between simulated and real environments. These dis parities, including unmodeled dynamics and variabilities in the real world, can undermine the effectiveness of the learned policy. Additionally, ensuring the generalization and robustness of the model poses a challenge. Models trained in deterministic simulations may struggle to adapt to the complexities and variability of real-world environments, potentially leading to over-specialization in simulation-specific features.