Personalized Observation Normalization for Federated Reinforcement Learning in Simulation Environments with Heterogeneity

In three linesPersonalized Observation Normalization (PON) method for federated reinforcement learning in heterogeneous environments. Each agent locally normalizes state inputs using continuously updated running mean and variance, preventing imbalanced parameter aggregation issues. Experiments on heterogeneous MuJoCo tasks demonstrate accelerated training and superior performance versus baselines.

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