Enhancing Deep Reinforcement Learning with Categorical Cross-Entropy Loss: A Study by Google DeepMind and Others

Overall, the research conducted by Google DeepMind and other researchers on training value functions with categorical cross-entropy loss in deep reinforcement learning shows promising results. By reframing regression as classification and utilizing cross-entropy loss, significant improvements in performance, scalability, and robustness have been achieved across various tasks and neural network architectures. This innovative approach has the potential to enhance the effectiveness of value-based RL methods and pave the way for more efficient learning algorithms in the future.