Litcius/Paper detail

Event-triggered reinforcement learning <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si49.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> control design for constrained-input nonlinear systems subject to actuator failures

Yuling Liang, Huaguang Zhang, Jie Duan, Shaoxin Sun

2020Information Sciences34 citationsDOI

Topics & Concepts

Reinforcement learningComputer scienceArtificial neural networkHamilton–Jacobi–Bellman equationControl theory (sociology)Sliding mode controlEvent (particle physics)AlgorithmLyapunov stabilityArtificial intelligenceOptimal controlControl (management)MathematicsMathematical optimizationNonlinear systemPhysicsQuantum mechanicsAdaptive Dynamic Programming ControlReinforcement Learning in Robotics
Event-triggered reinforcement learning <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si49.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> control design for constrained-input nonlinear systems subject to actuator failures | Litcius