[PRG] April 26 - Towards Practical Multi-object Manipulation using Relational Reinforcement Learning
Towards Practical Multi-object Manipulation using Relational Reinforcement Learning, by Li, Jabri, Darrell, and Agrawal
Created on April 19|Last edited on April 19
Comment
The W&B Paper Reading Group will next meet to discuss Towards Practical Multi-object Manipulation using Relational Reinforcement Learning by Richard Li, Allan Jabri, Trevor Darrell, and Pulkit Agrawal. Learn more and check out some demonstration videos of their simulated robot here.
The next day, we'll have Professor Agrawal on the W&B Salon to discuss this work, along with his other work on using to deep learning to create robots that can move and act in complex environments (details here).
After you've read the paper, if you have any questions or topics you'd like to discuss in the reading group, you can post them in the comment section below.
Here's our quick summary of the paper:
Reinforcement learning algorithms require an exorbitant number of interactions to learn from sparse rewards. To overcome this, the authors present a simple but effective method for learning from a curriculum of increasing number of objects. They show that attention-based graph neural networks provide critical inductive biases that enable usage of this task curriculum. Their agent achieves a success rate of 75% for stacking 6 blocks, while the existing state-of-the-art method, which uses human demonstrations and resets, only achieves a success rate of 32%.
Add a comment