Curiosity and Procrastination in Reinforcement Learning

Curiosity and Procrastination in Reinforcement Learning

  • October 25, 2018
Table of Contents

Curiosity and Procrastination in Reinforcement Learning

Episodic Curiosity through Reachability: Observations are added to memory, reward is computed based on how far the current observation is from the most similar observation in memory. The agent receives more reward for seeing observations which are not yet represented in memory.

Source: googleblog.com

Share :
comments powered by Disqus

Related Posts

California Law Bans Bots From Pretending to Be Human

California Law Bans Bots From Pretending to Be Human

Are you talking to a real person online or a bot? In California, bots will need to identify themselves thanks to a new bill just signed into law by Gov. Jerry Brown. The measure bans automated accounts from pretending to be real people in order to ‘incentivize a purchase or sale of goods or services in a commercial transaction or to influence a vote in an election,’ effective July 1, 2019.

Read More
Carnegie Mellon Researchers Develop New Deepfake Method

Carnegie Mellon Researchers Develop New Deepfake Method

Deepfakes, ultrarealistic fake videos manipulated using machine learning, are getting pretty convincing. And researchers continue to develop new methods to create these types of videos, for better or, more likely, for worse. The most recent method comes from researchers at Carnegie Mellon University, who have figured out a way to automatically transfer the “style” of one person to another.

Read More