DDPG FOR TRAJECTORY GENERATION

Abstract

. Deep Deterministic Policy Gradient (DDPG) is an effective reinforcement learning method designed for continuous control tasks. In this work, we investigate the use of DDPG to generate optimal motion trajectories for robotic systems, focusing on smooth, efficient, and collision-free movements. The approach centers on designing a reward function that balances trajectory smoothness, successful task execution, and obstacle avoidance. A simulated training environment is constructed to develop and assess the learned policies. Results from the experiments show that DDPG is capable of producing high-quality trajectories. These outcomes emphasize the promise of deep reinforcement learning in enabling real-time trajectory optimization for robotic applications.