Research
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[Lecture 8] (1/2) Planning and ModelsResearch/RL_DeepMind 2024. 8. 3. 17:11
https://storage.googleapis.com/deepmind-media/UCL%20x%20DeepMind%202021/Lecture%208%20-%20Model%20Based%20Reinforcement%20Learning.pdfhttps://www.youtube.com/watch?v=FKl8kM4finE&list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm&index=8If we look back at dynamic programming and model-free algorithms, we can roughly sketch the underlying principles and differences between the two in the following way. So i..
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[Lecture 7] (2/2) Function ApproximationResearch/RL_DeepMind 2024. 8. 3. 09:55
https://storage.googleapis.com/deepmind-media/UCL%20x%20DeepMind%202021/Lecture%207-%20Function%20approximation%20in%20reinforcement%20learning%20.pdfhttps://www.youtube.com/watch?v=ook46h2Jfb4&list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm&index=7Now we can't update towards the true value function if we don't have that yet, so instead we're going to substitute the targets. For Monte Carlo, we could p..
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[Lecture 7] (1/2) Function ApproximationResearch/RL_DeepMind 2024. 8. 3. 00:09
https://storage.googleapis.com/deepmind-media/UCL%20x%20DeepMind%202021/Lecture%207-%20Function%20approximation%20in%20reinforcement%20learning%20.pdfhttps://www.youtube.com/watch?v=ook46h2Jfb4&list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm&index=7The policy, value function, model, and agent state update, all of these things that are inside the agent can be viewed as being functions. For instance, a po..
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[Lecture 6] (2/2) Model-Free ControlResearch/RL_DeepMind 2024. 8. 2. 23:05
https://www.youtube.com/watch?v=t9uf9cuogBo&list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm&index=6&t=1068sSo that's one way to go. Monte Carlo learning and Temporal Difference learning. And in both cases what we're doing is, we're interleaving policy evaluation and policy improvement step. Now we're going to turn to a new topic which is Off-policy learning which is about learning about a policy differ..
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[Lecture 6] (1/2) Model-Free ControlResearch/RL_DeepMind 2024. 7. 31. 00:02
https://storage.googleapis.com/deepmind-media/UCL%20x%20DeepMind%202021/Lecture%206%20-%20Model-free%20control.pdfhttps://www.youtube.com/watch?v=t9uf9cuogBo&list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm&index=6Policy iteration refers to interleaving two separate steps which are called policy evaluation and policy improvement. We start with some arbitrary initial value function for instance they coul..
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[Lecture 5] Model-Free PredictionResearch/RL_DeepMind 2024. 7. 30. 15:54
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 ..
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[Lecture 4] Theoretical Fundamentals of Dynamic ProgrammingResearch/RL_DeepMind 2024. 7. 30. 14:47
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..
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[Lecture 3] Markov Decision Processes and Dynamic ProgrammingResearch/RL_DeepMind 2024. 7. 29. 18:11
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..