# Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2412.02316 · HTML: https://mlsanity.com/paper/2412.02316/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 3 Dec 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 11 of 11)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates](https://mlsanity.com/paper/1610.00633/) (2016)
- [A Survey and Critique of Multiagent Deep Reinforcement Learning](https://mlsanity.com/paper/1810.05587/) (2018)
- [Reward (Mis)design for Autonomous Driving](https://mlsanity.com/paper/2104.13906/) (2021)
- [Greed is Good: Near-Optimal Submodular Maximization via Greedy Optimization](https://mlsanity.com/paper/1704.01652/) (2017)
- [Learning-based Methods for Adaptive Informative Path Planning](https://mlsanity.com/paper/2404.06940/) (2024)
- [Designing Rewards for Fast Learning](https://mlsanity.com/paper/2205.15400/) (2022)
- [Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring](https://mlsanity.com/paper/2401.04631/) (2024)
- [Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning](https://mlsanity.com/paper/2402.04894/) (2024)

## Built on by (most cited first, 4 of 4)

- [Deep Reinforcement Learning for Urban Air Quality Management: Multi-Objective Optimization of Pollution Mitigation Booth Placement in Metropolitan Environments](https://mlsanity.com/paper/2505.00668/) (2025)
- [Beyond Task Performance: A Metric-Based Analysis of Sequential Cooperation in Heterogeneous Multi-Agent Destructive Foraging](https://mlsanity.com/paper/2602.10685/) (2026)
- [A Unified Experimental Architecture for Informative Path Planning: from Simulation to Deployment with GuadalPlanner](https://mlsanity.com/paper/2602.10702/) (2026)
- [Sim-to-Real Transfer and Robustness Evaluation of Reinforcement Learning Control with Integrated Perception on an ASV for Floating Waste Capture](https://mlsanity.com/paper/2605.02529/) (2026)

## Abstract (arXiv)

This paper presents a model-free deep reinforcement learning framework for informative path planning with heterogeneous fleets of autonomous surface vehicles to locate and collect plastic waste. The system employs two teams of vehicles: scouts and cleaners. Coordination between these teams is achieved through a deep reinforcement approach, allowing agents to learn strategies to maximize cleaning efficiency. The primary objective is for the scout team to provide an up-to-date contamination model, while the cleaner team collects as much waste as possible following this model. This strategy leads to heterogeneous teams that optimize fleet efficiency through inter-team cooperation supported by a tailored reward function. Different trainings of the proposed algorithm are compared with other state-of-the-art heuristics in two distinct scenarios, one with high convexity and another with narrow corridors and challenging access. According to the obtained results, it is demonstrated that deep reinforcement learning based algorithms outperform other benchmark heuristics, exhibiting superior adaptability. In addition, training with greedy actions further enhances performance, particularly in scenarios with intricate layouts.