  Enhanced sampling methods such as metadynamics and umbrella sampling have
become essential tools for exploring the configuration space of molecules and
materials. At the same time, they have long faced a number of issues such as
the inefficiency when dealing with a large number of collective variables (CVs)
or systems with high free energy barriers. In this work, we show that with
\redc{the clustering and adaptive tuning techniques}, the reinforced dynamics
(RiD) scheme can be used to efficiently explore the configuration space and
free energy landscapes with a large number of CVs or systems with high free
energy barriers. We illustrate this by studying various representative and
challenging examples. Firstly we demonstrate the efficiency of adaptive RiD
compared with other methods, and construct the 9-dimensional free energy
landscape of peptoid trimer which has energy barriers of more than 8 kcal/mol.
We then study the folding of the protein chignolin using 18 CVs. In this case,
both the folding and unfolding rates are observed to be equal to 4.30~$\mu
s^{-1}$. Finally, we propose a protein structure refinement protocol based on
RiD. This protocol allows us to efficiently employ more than 100 CVs for
exploring the landscape of protein structures and it gives rise to an overall
improvement of 14.6 units over the initial Global Distance Test-High Accuracy
(GDT-HA) score.
