Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning
Michelle A. Lee,Carlos Florensa,Jonathan Tremblay,Nathan Ratliff,Animesh Garg,Fabio Ramos,Dieter Fox,Michelle A. Lee,Carlos Florensa,Jonathan Tremblay,Nathan Ratliff,Animesh Garg,Fabio Ramos,Dieter Fox
Traditional robotic approaches rely on an accurate model of the environment, a detailed description of how to perform the task, and a robust perception system to keep track of the current state. On the other hand, reinforcement learning approaches can operate directly from raw sensory inputs with only a reward signal to describe the task, but are extremely sampleinefficient and brittle. In this wo...
