Visual model-based reinforcement learning as a path towards generalist robots by BAIR Blog. 04 December 2018 . share this: By Chelsea Finn ... Our goal is to build a generalist: a robot that can ...

Q1: How should we learn a model in model-based reinforcement learning? Model-based RL offers a promising approach to design sample efficient RL agents. But the conventional model learning methods may ...

To address the difficulty of designing a controller for complex visual-servoing tasks, two learning-based uncalibrated approaches are introduced. The first method starts by building an estimated model ...

Function approximation for RL (classic methods, Deep Learning) Deep RL : Deep Q- Networks (DQN), A3C, A2C, … Grade Breakdown (TBD) Assignments. Assignments will be programming based with some theory ...

Reinforcement learning is the study of decision making over time with consequences. The field has developed systems to make decisions in complex environments based on external, and possibly delayed, ...

For extracting deformation representations and learning to manipulate deformable tissues based on 2D images, we introduce a Sequential-information-based Contrastive State Representation Learning ...