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

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