2017
DOI: 10.1016/j.robot.2017.02.006
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Benchmarking model-free and model-based optimal control

Abstract: Model-free reinforcement learning and nonlinear model predictive control are two different approaches for controlling a dynamic system in an optimal way according to a prescribed cost function. Reinforcement learning acquires a control policy through exploratory interaction with the system, while nonlinear model predictive control exploits an explicitly given mathematical model of the system. In this article, we provide a comprehensive comparison of the performance of reinforcement learning and nonlinear model… Show more

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Cited by 19 publications
(12 citation statements)
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“…The longer prediction horizon used by CAL γ = 0.99 attains a better performance. Another problem of CAL is the slow convergence which is probably caused by the fact that the reward constructed from the quadratic objective function of the nominal controller results in small gradients [2]. This hypothesis is supported by the fact that DPG with a quadratic cost function learns the task extremely slowly.…”
Section: Discussionmentioning
confidence: 99%
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“…The longer prediction horizon used by CAL γ = 0.99 attains a better performance. Another problem of CAL is the slow convergence which is probably caused by the fact that the reward constructed from the quadratic objective function of the nominal controller results in small gradients [2]. This hypothesis is supported by the fact that DPG with a quadratic cost function learns the task extremely slowly.…”
Section: Discussionmentioning
confidence: 99%
“…Usually, the dynamics of physical systems are known, but various uncertainties do not allow achieving optimal performance with model-based control methods [2]. Whereas for the estimation of parametric uncertainties moving horizon estimation techniques [3] can often be employed, for structural uncertainties, such as backlash, Coulomb friction or wear and tear, this is not easily possible.…”
Section: Introductionmentioning
confidence: 99%
“…Then, a policy-based reinforcement-learning feedback controller can be used to learn the nonlinear model of a 4D-printed soft robot through experiments and simulation data to compensate for the uncertainties. The self-learning algorithms play a significant role in adaptive 4D-printed systems to optimise the controller commands based on the information acquired from the interaction with the environment via the 3D-printed sensors [138][139][140][141][142].…”
Section: Adaptive 4d-printed Systems Designmentioning
confidence: 99%
“…Nowadays, most robot manipulators need to possess the capacity of accurate and fast trajectory tracking. Trajectory tracking control is a key issue in the field of robot manipulator motion planning [1][2][3]. It aims to enable the joints or links of the robot manipulator to track the desired trajectory with ideal dynamic quality or to stabilize them in the specified position [4].…”
Section: Introductionmentioning
confidence: 99%