Optimizing Deep Reinforcement Learning for Adaptive Robotic Arm Control
This incremental improvement addresses efficiency in deep reinforcement learning for complex robotic tasks, benefiting researchers and practitioners in robotics.
The paper tackled hyperparameter optimization for SAC and PPO algorithms using TPE in robotic arm control, resulting in success rate improvements of 10.48 percentage points for SAC and 34.28 percentage points for PPO, with faster convergence requiring about 40K fewer episodes for PPO.
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.