Other work has studied model-based RL in the real world, again focusing on individual skills. A number of recent works have studied self-supervised robotic learning, where large-scale unattended data ...

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 ...

We show how to teach machines to paint like human painters, who can use a small number of strokes to create fantastic paintings. By employing a neural renderer in model-based Deep Reinforcement ...

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 ...

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, ...

Assignments Assignments will be programming based with some theory quetsions included as part of the report. Programming will be in python using straight python and later using tensorflow or keras.

This study seeks to construct a basic reinforcement learning-based AI-macroeconomic simulator. We use a deep RL (DRL) approach (DDPG) in an RBC macroeconomic model. We set up two learning scenarios, ...