Reinforcement Learning based Visual Navigation with Information-Theoretic Regularization

We present a target-driven navigation approach for improving the cross-target and cross-scene generalization for visual navigation. Our approach incorporates an information-theoretic regularization into a deep reinforcement learning (RL) framework. First, we present a supervised generative model to constrain the intermediate process of the RL policy, which is used to generate a future observation from a current observation and a target. Next, we predict a navigation action by analyzing the difference between the generated future and the current. Our approach takes into account the connection between current observations and targets, and the interrelation between actions and visual transformations. This results in a compact and generalizable navigation model. We perform experiments on the AI2-THOR framework and the Active Vision Dataset (AVD) and show at least 7.8% improvement in navigation success rate and 5.7% in SPL, compared to the supervised baseline, in unexplored environments.