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https://storage.googleapis.com/deepmind-media/UCL%20x%20DeepMind%202021/Lecture%205%20-%20ModelFreePrediction.pdfhttps://www.youtube.com/watch?v=eaWfWoVUTEw&list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm&index=5In general, in reinforcement learning when people say Monte Carlo, they typically mean sample complete episodes, an episode is a trajectory of experience which has some sort of a natural ending ..

https://storage.googleapis.com/deepmind-media/UCL%20x%20DeepMind%202021/Lecture%204%20-%20Theoretical%20Fundamentals%20of%20DP%20Algorithms.pdfhttps://www.youtube.com/watch?v=XpbLq7rIJAA&list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm&index=4For any two points in this vector space, if you apply this contraction mapping to this point, the distance between two points shrinks at least by alpha. For any seq..

https://storage.googleapis.com/deepmind-media/UCL%20x%20DeepMind%202021/Lecture%203%20-%20MDPs%20and%20Dynamic%20Programming.pdfhttps://www.youtube.com/watch?v=zSOMeug_i_M&list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm&index=3 Anything that has happened in this interaction process is summarized in the current state. In terms of its information about the future transition is summarized in the current st..

https://storage.googleapis.com/deepmind-media/UCL%20x%20DeepMind%202021/Lecture%202-%20Exploration%20and%20control_slides.pdfhttps://www.youtube.com/watch?v=aQJP3Z2Ho8U&list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm&index=2One interesting property that we're going to talk about a lot is that it's an active learning setting. This means that these actions they don't just change the reward. They don't jus..

https://storage.googleapis.com/deepmind-media/UCL%20x%20DeepMind%202021/Lecture%201%20-%20introduction.pdfhttps://www.youtube.com/watch?v=TCCjZe0y4Qc&list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm&index=1 http://incompleteideas.net/book/the-book-2nd.htmlYou are subjected to some data or experience, but the experience is not fully out of your control. The actions that you take might influence the experi..

https://arxiv.org/pdf/1904.10509AbstractTransformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length. In this paper we introduce sparse factorizations of the attention matrix which reduce this to O(n √ n). We also introduce a) a variation on architecture and initialization to train deeper networks, b) the recomputation of attention matr..

https://arxiv.org/pdf/2005.11401AbstractLarge pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely manipulate knowledge is still limited, and hence on knowledge-intensive tasks, their performance lags behind task-specific architecture..

https://arxiv.org/pdf/2203.11171AbstractChain-of-thought prompting combined with pre-trained large language models has achieved encouraging results on complex reasoning tasks. In this paper, we propose a new decoding strategy, self-consistency, to replace the naive greedy decoding used in chain-of-thought prompting. It first samples a diverse set of reasoning paths instead of only taking the gre..