Improving Generalization in Reinforcement Learning with Mixture Regularization

Understand

Deep reinforcement learning (RL) agents trained in a limited set of environments tend to suffer overfitting and fail to generalize to unseen testing environments.

• To improve their generalizability, data augmentation approaches (e.g.
• cutout and random convolution) are previously explored to increase the data diversity.
• However, we find these approaches only locally perturb the observations regardless of the training environments, showing limited effectiveness on enhancing the data diversity and the generalization performance.