PERCEPTUAL GOAL SPECIFICATIONS FOR REINFORCEMENT LEARNING

By Ashley D. Edwards. 2017

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Sergey Levine
2. Chelsea Finn
3. Tianhe Yu
4. Avi Singh
5. Pieter Abbeel
6. Suraj Nair
7. Abhishek Gupta
8. Han-Jia Ye

Citations in the corpus

• End-to-End Robotic Reinforcement Learning without Reward Engineering (2019, background): “Data-driven approaches for reward specification [29, 1, 11, 17, 12, 48, 33, 9, 27, 4, 18] seek to overcome this issue, but typically require demonstration data to acquire rewards.”
• Unsupervised Visuomotor Control through Distributional Planning Networks (2019, background): “Motivated by this problem, one appealing alternative approach is to provide an example image of a desired goal state [10, 48, 15, 43, 34, 11], and derive a reward function using the goal image.”