An introduction to Reinforcement Learning
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
If you enjoy my videos, all support is super welcome!
https://www.patreon.com/ArxivInsights
If you have questions you would like to discuss with me personally, you can book a 1-on-1 video call through Pensight: https://pensight.com/x/xander-steenbrugge
::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?
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
If you enjoy my videos, all support is super welcome!
https://www.patreon.com/ArxivInsights
If you have questions you would like to discuss with me personally, you can book a 1-on-1 video call through Pensight: https://pensight.com/x/xander-steenbrugge
::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?
