Analysis of the Application of Reinforcement Learning in Motion Control of Robots in Unstructured Environments

Currently, motion control for robots in unstructured environments suffers from insufficient adaptability and robustness, and traditional methods rely on precise models that are prone to failure. Therefore, this paper focuses on the application and optimization of reinforcement learning in this scenario. Existing research has shown that balancing exploration efficiency and stability can be achieved through basis functions, teacher-student models, and domain randomization; the reward function can be optimized using an improved SAC algorithm to construct more accurate state modeling; and multi-layer convolutional networks and attention mechanisms can be used to fuse multi-source perceptual features. A complete technical framework has been constructed, research gaps have been identified, and a reference has been provided for theoretical innovation and engineering implementation of motion control for robots in unstructured environments.