11h ago
K-Score: Kalman Filter as a Principled Alternative to Reward Normalization in Reinforcement Learning
★★★★★
significance 2/5
Researchers propose using a 1D Kalman filter as a principled alternative to standard reward normalization in reinforcement learning. The method helps smooth high-variance returns and adapt to non-stationary environments with minimal computational overhead. Experimental results on LunarLander and CartPole show improved convergence and reduced training variance.
Why it matters
Replacing heuristic reward normalization with Kalman filters offers a more robust, mathematically grounded approach to stabilizing training in non-stationary environments.
Tags
#reinforcement learning #kalman filter #reward normalization #policy gradientRelated coverage
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