Editing Reinforcement learning (section)

=== Stability and Convergence Issues ===
Training RL models, particularly for [[Deep learning|deep neural network-based models]], can be unstable and prone to divergence. A small change in the policy or environment can lead to extreme fluctuations in performance, making it difficult to achieve consistent results. This instability is further enhanced in the case of the continuous or high-dimensional action space, where the learning step becomes more complex and less predictable.