Regularized Parameter Uncertainty for Improving Generalization in Reinforcement Learning

In order for reinforcement learning (RL) agents to be deployed in real-world environments, they must be able to generalize to unseen environments. However, RL struggles with out-of-distribution generalization, often due to over-fitting the particulars of the training environment. Although regularization techniques from supervised learning can be applied to avoid over-fitting, the differences between supervised learning and RL limit their application. To address this, we propose the Signal-to-Noise Ratio regulated Parameter Uncertainty Network (SNR PUN) for RL. We introduce SNR as a new measure of regularizing the parameter uncertainty of a network and provide a formal analysis explaining why SNR regularization works well for RL. We demonstrate the effectiveness of our proposed method to generalize in several simulated environments; and in a physical system showing the possibility of using SNR PUN for applying RL to real-world applications.