Implementing batch updates in reinforcement learning enhances stability and accelerates training by processing multiple samples simultaneously. Utilizing TensorFlow's features, such as target networks and policy gradient methods, improves convergence and reduces variance. Optimizing hyperparameters, including learning rates and batch sizes, is crucial for effective model performance.