  The development of robotic systems for palletization in logistics scenarios
is of paramount importance, addressing critical efficiency and precision
demands in supply chain management. This paper investigates the application of
Reinforcement Learning (RL) in enhancing task planning for such robotic
systems. Confronted with the substantial challenge of a vast action space,
which is a significant impediment to efficiently apply out-of-the-shelf RL
methods, our study introduces a novel method of utilizing supervised learning
to iteratively prune and manage the action space effectively. By reducing the
complexity of the action space, our approach not only accelerates the learning
phase but also ensures the effectiveness and reliability of the task planning
in robotic palletization. The experimental results underscore the efficacy of
this method, highlighting its potential in improving the performance of RL
applications in complex and high-dimensional environments like logistics
palletization.
