  This study introduces the P5 model - a foundational method that utilizes
reinforcement learning (RL) to augment control, effectiveness, and scalability
in molecular dynamics simulations (MD). Our innovative strategy optimizes the
sampling of target polymer chain conformations, marking an efficiency
improvement of over 37.1%. The RL-induced control policies function as an
inductive bias, modulating Brownian forces to steer the system towards the
preferred state, thereby expanding the exploration of the configuration space
beyond what traditional MD allows. This broadened exploration generates a more
varied set of conformations and targets specific properties, a feature pivotal
for progress in polymer development, drug discovery, and material design. Our
technique offers significant advantages when investigating new systems with
limited prior knowledge, opening up new methodologies for tackling complex
simulation problems with generative techniques.
