Learning to Identify Critical States for Reinforcement Learning from
  Videos
Haozhe Liu Mingchen Zhuge Bing Li Yuhui Wang Francesco Faccio Bernard Ghanem Jürgen Schmidhuber
Recent work on deep reinforcement learning (DRL) has pointed out that
algorithmic information about good policies can be extracted from offline data
which lack explicit information about executed actions. For example, videos of
humans or robots may convey a lot of implicit information about rewarding
action sequences, but a DRL machine that wants to profit from watching such
videos must first learn by itself to identify and recognize relevant
states/actions/rewards. Without relying on ground-truth annotations, our new
method called Deep State Identifier learns to predict returns from episodes
encoded as videos. Then it uses a kind of mask-based sensitivity analysis to
extract/identify important critical states. Extensive experiments showcase our
method's potential for understanding and improving agent behavior. The source
code and the generated datasets are available at
https://github.com/AI-Initiative-KAUST/VideoRLCS.