mlsanityData to 14 Oct 2026

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

arXiv 2412.02316 ↗First posted 3 Dec 2024cs.RO cs.LGcited by 4

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

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.

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.

Builds on

11 of 32 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning7 Feb

Other references · 10

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2015Prioritized Experience Replaycited by 1,099
  3. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  4. 2016Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updatescited by 353
  5. 2018A Survey and Critique of Multiagent Deep Reinforcement Learningcited by 177
  6. 2021Reward (Mis)design for Autonomous Drivingcited by 104
  7. 2017Greed is Good: Near-Optimal Submodular Maximization via Greedy Optimizationcited by 11
  8. 2024Learning-based Methods for Adaptive Informative Path Planningcited by 10
  9. 2022Designing Rewards for Fast Learningcited by 7
  10. 2024Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoringcited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Deep Reinforcement Learning for Urban Air Quality Management: Multi-Objective Optimization of Pollution Mitigation Booth Placement in Metropolitan Environmentscited by 0
  2. 2026Beyond Task Performance: A Metric-Based Analysis of Sequential Cooperation in Heterogeneous Multi-Agent Destructive Foragingcited by 0
  3. 2026A Unified Experimental Architecture for Informative Path Planning: from Simulation to Deployment with GuadalPlannercited by 0
  4. 2026Sim-to-Real Transfer and Robustness Evaluation of Reinforcement Learning Control with Integrated Perception on an ASV for Floating Waste Capturecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024A Deep Reinforcement Learning Framework and Methodology for Reducing the Sim-to-Real Gap in ASV Navigation
  2. 2024An Efficient Multi-Robot Arm Coordination Strategy for Pick-and-Place Tasks using Reinforcement Learning
  3. 2025Reinforcement Learning for Pollution Detection in a Randomized, Sparse and Nonstationary Environment with an Autonomous Underwater Vehicle
  4. 2024Solving a Real-World Optimization Problem Using Proximal Policy Optimization with Curriculum Learning and Reward Engineering
  5. 2025Evaluating Robustness of Deep Reinforcement Learning for Autonomous Surface Vehicle Control in Field Tests
  6. 2025The Emergence of Deep Reinforcement Learning for Path Planning
  7. 2025Curriculum RL meets Monte Carlo Planning: Optimization of a Real World Container Management Problem
  8. 2025Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning