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

The paper introduces a personalized observation normalization (PON) method for federated reinforcement learning (FedRL) to address challenges in heterogeneous environments. PON allows each agent to locally normalize state inputs, ensuring consistent scaling and improving performance in heterogeneous MuJoCo tasks.