This episode gives a general introduction into the field of Reinforcement Learning:
- High level description of the field
- Policy gradients
- Biggest challenges (sparse rewards, reward shaping, ...)

This video forms the basis for a series on RL where I will dive much deeper into technical details of state-of-the-art methods for RL.

Links:
- "Pong from Pixels - Karpathy": http://karpathy.github.io/2016/05/31/rl/
- Concept networks for grasp & stack (Paper with heavy reward shaping): https://arxiv.org/abs/1709.06977

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::Chapters::
00:00 Intro
01:03 So what is Reinforcement Learning?
03:39 Learning without explicit examples
07:25 Main challenges when doing RL
15:04 Are the robots taking over now?