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Monte Carlo And Off-Policy Methods | Reinforcement Learning Part 3

The machine learning consultancy: https://truetheta.io Join my email list to get educational and useful articles (and nothing else!): https://mailchi.mp/truetheta/true-the... Want to work together? See here: https://truetheta.io/about/#want-to-w... Part three of a six part series on Reinforcement Learning. It covers the Monte Carlo approach a Markov Decision Process with mere samples. At the end, we touch on off-policy methods, which enable RL when the data was generate with a different agent. SOCIAL MEDIA LinkedIn :   / dj-rich-90b91753   Twitter :   / duanejrich   Github: https://github.com/Duane321 Enjoy learning this way? Want me to make more videos? Consider supporting me on Patreon:   / mutualinformation   SOURCES [1] R. Sutton and A. Barto. Reinforcement learning: An Introduction (2nd Ed). MIT Press, 2018. [2] H. Hasselt, et al. RL Lecture Series, Deepmind and UCL, 2021,    • DeepMind x UCL | Deep Learning Lectur...   SOURCE NOTES The video covers topics from chapters 5 and 7 from [1]. The whole series teaches from [1]. [2] has been a useful secondary resource. TIMESTAMP 0:00 What We'll Learn 0:33 Review of Previous Topics 2:50 Monte Carlo Methods 3:35 Model-Free vs Model-Based Methods 4:59 Monte Carlo Evaluation 9:30 MC Evaluation Example 11:48 MC Control 13:01 The Exploration-Exploitation Trade-Off 15:01 The Rules of Blackjack and its MDP 16:55 Constant-alpha MC Applied to Blackjack 21:55 Off-Policy Methods 24:32 Off-Policy Blackjack 26:43 Watch the next video! NOTES Link to Constant-alpha MC applied to Blackjack: https://github.com/Duane321/mutual_in... The Off-Policy method you see at 25:00 is different from the rule you'll see in the textbook at eq 7.9 (which will be MC if n goes to inf). That's because they are showing re-weighted IS and I'm showing plain ( high variance) IS.