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UKAEA-RACE-PR(25)032024
When using deep reinforcement learning (DRL) to perform multi-robot exploration in unknown environments, the training model may produce actions that lead to unpredictable system behaviours due to the complexity and unpredictability of the surroundings. Therefore, ensuring safe exploration with DRL becomes critical. To tackle this issue, we propo…
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UKAEA-RACE-CP(23)062023
Sim-and-real training is a promising alternative to sim-to-real training for robot manipulations. However, the current sim-and-real training is neither efficient, i.e., slow convergence to the optimal policy, nor effective, i.e., sizeable real-world robot data. Given limited time and hardware budgets, the performance of sim-and-real training is …
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UKAEA-CCFE-PR(23)1522023
This article presents an in-depth study of the sequence of events leading to density limit disruption in J-TEXT tokamak plasmas, with an emphasis on boudary turbulent transport and the high-field-side high-density (HFSHD) front. These phenomena were extensively investigated by using Langmuir probe and Polarimeter-interferometer diagnostics. The res…
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2016
Cold and hot vertical displacement events (VDEs) are frequently related to the disruption of vertically-elongated tokamaks. The weak poloidal magnetic field around the null-points of a snowflake divertor configuration may influence the vertical displacement process. In this paper, the major disruption with a cold VDE and the vertical disruption in …
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