RL Course by David Silver - Lecture 1: Introduction to Reinforcement Learning

Sutton & Barto's Reinforment Learning complete this triumvirate link David Silver's Reinforcement Course is based on Sutton & Barto this video

You can check this Reinforcement Learning Course by David Silver on YouTube: this video By the way, I believe David Silver was the lead programmer for AlphaZero.

fast.ai Making neural nets uncool again. fast.ai is dedicated to making the power of deep learning accessible to all.[0] Udacity. Intro to Machine Learning: Pattern Recognition for Fun and Profit[1] MIT 6.S099: Artificial General Intelligence[2] Lecture Series on Artificial Intelligence by Prof. P. Dasgupta, Department of Computer Science & Engineering, Indian Institute of Technology, Kharagpur[3] DeepMind. Reinforcement Learning Course by David Silver[4] Stanford Winter Quarter 2016 class: CS231n: Convolutional Neural Networks for Visual Recognition.[5] [0] link [1] link [2] link [3] link [4] this video [5] link

As someone who is doing his bachelor thesis on Reinforcement Learning this is some useful information. OT (but not really) question: does anyone here use Reinforcement Learning techniques at work? For the thesis I am working on black-box optimization of 2 variable functions with Reinforcement Learning (and comparing it with Bayesian Optimization techniques). As someone else suggested the Sutton & Barto book is really great knowledge but I would also like to suggest these lessons ( this video ) by David Silver (who worked on AlphaGo)

The notions of agent, environment and reward are encountered in Reinforcement Learning which is a sub-field of ML but also relevant to biological agents. There is a great RL course on YouTube by David Silver (one of the creators of AlphaGo, which is probably the most famous application of RL so far). this video The course can properly set the perspective of RL and shed new light on the philosophy of mind, if you take what it says and then extrapolate to humans and other agents. What I find fascinating about RL is that it can be defined concretely. Consciousness can only be defined by reference to other words, in a less exact and concrete way. RL can also explain how meaning appears, based on future reward prediction. The rich sensations we have can be explained by an encoding-decoding architecture based on reconstruction error. Many difficulties in RL map back to difficulties humans have in choosing how to act - the exploration-exploitation tradeoff, instinct vs reason (two different ways to perform RL, one based purely on rewards and the other based on an internal model of the world and rewards). Some of the problems related to multi-agent RL are also covered in Game Theory, such as the prisoner's dilemma. Regarding philosophy of language: we have today numerical representations of meaning in words and phrases. They are usually represented as vectors in high dimensional space or sets of vectors. For example it is customary to use 300-1000 dimensions for representing words. Thes

I came across this a few days ago: For Reinforcement Learning specifically, the standard text is Reinforcement Learning: An Introduction[1]. Dave's UCL Course on RL[2] is great too (playlist of all lectures)[3]. Source: Julian Schrittwieser works on Deepmind at Google link [1] link [2] link [3] this video

I also recommend interested people to watch David Silver's RL lectures at UCL on YouTube. He covers material from the book. this video

I would start with David Silvers (DeepMind) youtube series to get an idea of what's possible or not. Running an already trained reinforcement learning agent is relatively cheap (unless your model is massive). I suspect the reason people aren't using it yet is because it's a) really difficult to get right in training, even basic convergence is not guaranteed without careful tuning b) really difficult to guarantee reasonable behavior outside of the scenarios you're able to reach in QA. edit: Link to lecture series this video