Towards Smart Manufacturing using Reinforcement Learning in a Sparse-Rewarded Environment for Through-Hole Technology

Advance automation, digitization, and interconnectivity are driving rapid evolution in the manufacturing industry. As a result, smart manufacturing has become increasingly popular, driven by the integration of deep reinforcement learning (DRL) and digital twins in agent training. DRL has proven to be particularly effective in smart manufacturing, allowing agents to respond dynamically to an ever-changing environment with a wide range of manufacturing components which leads to a slight variation in the objectives. This study proposes an efficient DRL approach to smart manufacturing tasks by breaking them into three simpler tasks: reach, push, and pick-and-place. The Truncated Quantile Critics (TQC) algorithm is employed as the learning algorithm for the agent, which is trained using Hindsight Experience Replay (HER) to formulate virtual goals in a sparserewarded environment. Our experiments on these environments demonstrate that the proposed method of TQC+HER achieves the best results on all three of the smart manufacturing tasks.