A DRL controller for ASV floating waste capture, trained in simulation with a perception abstraction module, achieves centimeter-level accuracy in real-world field experiments across 14 disturbance regimes.
Proximal Policy Optimization Algorithms
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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REINFORCE, A2C, and PPO are compared for service rate control in an M/M/1 queue modeled as an SMDP, using queue length states and assessing convergence and regret.
citing papers explorer
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Sim-to-Real Transfer and Robustness Evaluation of Reinforcement Learning Control with Integrated Perception on an ASV for Floating Waste Capture
A DRL controller for ASV floating waste capture, trained in simulation with a perception abstraction module, achieves centimeter-level accuracy in real-world field experiments across 14 disturbance regimes.
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Empirical Evaluation of Policy-Based Reinforcement Learning for Dynamic Service Control in an M/M/1 Queue
REINFORCE, A2C, and PPO are compared for service rate control in an M/M/1 queue modeled as an SMDP, using queue length states and assessing convergence and regret.