  Reinforcement learning (RL) has shown promising results for real-time control
systems, including the domain of plasma magnetic control. However, there are
still significant drawbacks compared to traditional feedback control approaches
for magnetic confinement. In this work, we address key drawbacks of the RL
method; achieving higher control accuracy for desired plasma properties,
reducing the steady-state error, and decreasing the required time to learn new
tasks. We build on top of \cite{degrave2022magnetic}, and present algorithmic
improvements to the agent architecture and training procedure. We present
simulation results that show up to 65\% improvement in shape accuracy, achieve
substantial reduction in the long-term bias of the plasma current, and
additionally reduce the training time required to learn new tasks by a factor
of 3 or more. We present new experiments using the upgraded RL-based
controllers on the TCV tokamak, which validate the simulation results achieved,
and point the way towards routinely achieving accurate discharges using the RL
approach.
