Leveraging Symmetry in Dynamics for Efficient Model-Based Reinforcement Learning with Asymmetric Rewards

By exploiting symmetries in the dynamical model, independent of the reward function, this work presents a method to learn more accurate dynamical models that can improve sample efficiency in model-based reinforcement learning.