3 accepted papers
Virtually all robot control methods benefit from the availability of an accurate mathematical model of the robot. However, obtaining a sufficient amount of informative data for constructing dynamic models can be difficult, especially when the models are to be learned during robot deployment. Under s…
Reinforcement learning (RL) is a suitable approach for controlling systems with unknown or time-varying dynamics. RL in principle does not require a model of the system, but before it learns an acceptable policy, it needs many unsuccessful trials, which real robots usually cannot withstand. It is we…
It is well known that reinforcement learning (RL) can benefit from the use of a dynamic prediction model which is learned on data samples collected online from the process to be controlled. Most RL algorithms are formulated in the state-space domain and use state-space models. However, learning stat…