  In this paper, we explore the optimization of hyperparameters for the Soft
Actor-Critic (SAC) and Proximal Policy Optimization (PPO) algorithms using the
Tree-structured Parzen Estimator (TPE) in the context of robotic arm control
with seven Degrees of Freedom (DOF). Our results demonstrate a significant
enhancement in algorithm performance, TPE improves the success rate of SAC by
10.48 percentage points and PPO by 34.28 percentage points, where models
trained for 50K episodes. Furthermore, TPE enables PPO to converge to a reward
within 95% of the maximum reward 76% faster than without TPE, which translates
to about 40K fewer episodes of training required for optimal performance. Also,
this improvement for SAC is 80% faster than without TPE. This study underscores
the impact of advanced hyperparameter optimization on the efficiency and
success of deep reinforcement learning algorithms in complex robotic tasks.
