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Deepmind – Reinforcement Learning Lecture Series (2021)
- fnbr 4y agoI think this is an excellent resource. I used to work at DeepMind, and Hado & Matteo are two of the best RL researchers there.
- mmq 4y agoDavid Silver's reinforcement learning lecture series is excellent as well: https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLzuuYNsE1EZAXYR4FJ75jcJseBmo4KQ9- https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLzuuYNsE1E...
- ayhanfuat 4y agoThis is a more up to date version of David Silver’s course and judging from the few lectures I watched, it is very good but if you haven’t, I recommend having a look at Silver’s lectures, too. He is an amazing teacher and it is a joy to watch him explain something. https://www.deepmind.com/learning-resources/introduction-to-reinforcement-learning-with-david-silver https://www.deepmind.com/learning-resources/introduction-to-...
- huevosabio 4y agoIn the past, I was very skeptical of NLP efforts. Thought it was mostly full of cool toys. I was dead wrong. I am similarly skeptical of RL, in the sense that for most cases you are better of using optimal control techniques, and maybe sometimes a combination of RL and optimal control. I am aware of AlphaZero and other impressive achievements in certain games. However, I am still left with the feeling that it is very expensive to train an RL model and it is insanely specific to the task at hand. Are there recent breakthroughs that point at promising generalization in the RL community?
- qumpis 4y agoDreamer v3 in model-based direction had some interesting scaling plots showing a pattern of faster (per-sample) learning using bigger models. In terms of generalization, Ada by Deepmind was also quite impressive, but operated within simplistic world of tasks
- pilooch 4y agoBreakthrough may well be among the recent reformulations into supervised, offline RL and related techniques. Of special interest, the decision transformer and the very emerging litterature on diffusion planning.