Arxiv Insights
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
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I've been literally looking all over for a video like this, thank you so much
Your channel is a great resource for getting into Deep Learning and AI.
awesome video sir. please keep up the good work in this field….
Thank you so much for your videos!
amazing take I was not sure anyone could make it any easy to get ,good job mate
Excellent video
Good Video explaining reinforcement learning
You can't fade in the music while you are still talking because it makes it difficult to concentrate on the material.
What a fantastic channel!! ?
I really enjoyed your video, Thank youu!!
Anyone who can tell me the name of this cool guy ? Thanks
Thank you, very informative video!
Great video!!!! Explained exceptionally, liked other videos as well from your channel. Would love to see more stuff related to AI/DL or RL. Thanks in advance. Keep up the good work….
Great job.. Explained the subject in a simple way. Keep it up and looking forward for new videos
your videos are some of the best explanations I've found for a lot of these very advanced subjects. I suspect your viewer count is going to jump very quickly. keep it up.
good video and impressive explanation. i would like to get your mail ID please
OpenAI has completed Montezuma's Revenge and discovered all 24 rooms recently with RND-technique.
https://blog.openai.com/reinforcement-learning-with-prediction-based-rewards/
my new favorite channel
Well explained! Kudos!
why your head is too big ? 😀
I loved the way you explained everything. Thanks!
Excellent video, but ur hands movements are very distracting.
Rewards and Reinforcements need clarification. A reward is focused on a result, and a reinforcement is focused on behavior. Sometimes the difference is very subtle and hence the confusion, but the outcome is significantly different.
Montezuma’s Revenge has actually been solved recently . Figured there might be some people here that would find this interesting 🙂
https://eng.uber.com/go-explore/?amp
I've to submit a report on this topic 2 days from now. I'm a CS student of first semester and this was really difficult for me to understand. Can anyone help me ?
When I was a psychology student when trained chickens using reinforcement training with reward shaping. However it was a form supervised training in reality
Thanks!
Also your intro is very high quality, like an intro to a good TV show
<3
wait, so a student can never be better than its teacher? is that your logic @3:10 ?
perfect thank you very much
Nope, the media doesnt focus on the negative sides of AI because they fear what they cant understand. We do it (I am a journalist) because it bad news sell more than good news.
Very lively and understandable. Great work!
The perils of reward shaping are well understood in a public policy context, where incentives can lead to "unintended consequences".
Very nice episode! One thing that struck me about your suggestion that without Reward Shaping, the auto-learning of the 2600 games would be intractable: even for a human, this would be extremely difficult – we succeed with new, undocumented games because they often have similar sub-components and sub-goals that we already know from other games (or life). But I'm sure you could easily construct a game which would be impossible for a human to learn without any hints, while still having the same overall complexity.
your videos are really good but the subscribers and views should be more on such a good channel
Why don’t just recreate the loosing scene and playing all the opportunity
Amazing video. Keep up the good work and soon your channel will explode!