K-Score: Kalman Filter as a Principled Alternative to Reward Normalization in Reinforcement Learning

ArXi:2604.23056v1 Announce Type: new We propose a simple yet effective alternative to reward normalization in policy gradient reinforcement learning by integrating a 1D Kalman filter for online reward estimation. Instead of relying on fixed heuristics, our method recursively estimates the latent reward mean, smoothing high-variance returns and adapting to non-stationary environments. This approach incurs minimal overhead and requires no modification to existing policy architectures.