Active Exploration in Bayesian Model-based Reinforcement Learning Improves Sample Efficiency for Robot Manipulation Tasks

Leveraging Bayesian neural network models and active exploration strategies can significantly improve the sample efficiency of model-based reinforcement learning for robot manipulation tasks, outperforming model-free and reactive exploration approaches.