University of Cambridge > > Engineering Safe AI > Safe Exploration in Reinforcement Learning

Safe Exploration in Reinforcement Learning

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact AdriĆ  Garriga Alonso.

In reinforcement learning, the learning agent always faces a tradeoff between exploration and exploitation. Often, exploration is implemented as taking random actions, but in dangerous tasks, this can lead to highly negative rewards – damage to the agent or other parts of the environment. This week we discuss methods to minimize the risks in exploration and learn good policies both safely and efficiently.


This talk is part of the Engineering Safe AI series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


© 2006-2023, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity