Deep Q-learning with separate scout and cleaner policies outperforms greedy, PSO, and random baselines for simulated plastic-waste collection, with the largest gains in complex port layouts.
Deep reinforcement learning algorithms for path planning domain in grid-like environment,
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Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning
Deep Q-learning with separate scout and cleaner policies outperforms greedy, PSO, and random baselines for simulated plastic-waste collection, with the largest gains in complex port layouts.