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InfoResearchPeer-reviewed
Robustness-Guaranteed Reinforcement Learning Under Uncertainties in Dynamics Modeling and State Estimates
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Summary
This paper presents a reinforcement learning framework that guarantees robustness under bounded uncertainties in dynamics modeling and state estimates. The uncertainties and system dynamics are described with ReLU neural networks, which let the method find the states that most violate the robustness conditions and prioritize them during training. The authors evaluate the approach in simulation, including 3-D quadrotor reference trajectory tracking.