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Thanks. Also available from the same author: "Lessons from AlphaZero, for Optimal, Model Predictive, and Adaptive Control" web.mit.edu/dimitrib/www/R…

Our new paper “Semilinear Dynamic Programming" arxiv.org/abs/2501.04668 deals with a new class of linearly structured problems with nice analytical and computational properties, such as certainty equivalence, and potential applications in #reinforcementlearning

My #reinforcementlearning Spring 2025 course at ASU starts tomorrow. Slides and videolectures to be posted on Fridays at the course website web.mit.edu/dimitrib/www/R… which also contains the 475 pp textbook and course material from past years' offerings

Brilliant move: Mathematician's latest gambit is new chess AI | ASU News - Long before Bertsekas became a luminary in mathematics and computer science, authoring foundational textbooks on reinforcement learning, a type of ... - ift.tt/8ifyqQ2

My videolecture on computer chess describes a meta-chess architecture MPC-MC that improves the play of any chess engine (including Stockfish Chess and one from Google DeepMind) using #reinforcementlearning See youtube.com/watch?v=88LDkH…

This is an amazing playlist to learn Reinforcement Learning by Dimitri Bertsekas

A video lecture on #reinforcementlearning was posted at youtube.com/watch?v=mEjlnY… Originally delivered at an IEEE Symposium on ADPRL, Orlando, 2014. Several of the ideas await further exploration. Slides at mit.edu/~dimitrib/Gen_…

This week's #PaperILike is "Model Predictive Control and Reinforcement Learning: A Unified Framework Based on Dynamic Programming" (Bertsekas 2024). If you know 1 of {RL, controls} and want to understand the other, this is a good starting point. PDF: arxiv.org/abs/2406.00592

Just posted a videolecture on a Viterbi-like rollout/#reinforcementlearning algorithm for most likely sequence generation in Markov chains, and HMM inference, at youtu.be/KdX6o9Qi1vc Applies to large state spaces where the Viterbi algorithm is intractable

A recording of my guest lecture at ASU on aggregation for approximating POMDPs is available here: youtube.com/watch?v=gsD2Jg… Thanks to Dimitri Bertsekas and Yuchao Li for organizing this!

A free PDF of my book "Rollout, Policy Iteration, and #ReinforcementLearning " has been posted at my web site web.mit.edu/dimitrib/www/d… An extensive research account on rollout algorithms, including multiagent rollout, and the connection with Newton's method

I am pleased to share the video from my yesterday's lecture "Abstract Dynamic Programming, Reinforcement Learning, Newton's Method, and Gradient Optimization" at the ASU Math Dept youtube.com/watch?v=JmQzj0… This is an overview lecture on the relations between DP and RL

I am pleased to share the full set of videolectures, slides, textbook, and other supporting material of the 7th offering of my Reinforcement Learning class at ASU, which was completed two days ago; check web.mit.edu/dimitrib/www/R…

I am often asked about the relative merits of various #reinforcementlearning approaches, such as policy gradient and value-based methods. The last lecture of my RL course deals with this question, and related training issues, see: youtube.com/watch?v=43CXjD…

I am pleased to share podcasts (<30 mins) describing two of my books: Neuro-Dynamic Programming. notebooklm.google.com/notebook/c21b0… A Course in Reinforcement Learning notebooklm.google.com/notebook/a4a87… Free PDF of both books can be found at web.mit.edu/dimitrib/www/b…

I am pleased to share at web.mit.edu/dimitrib/www/R… High quality AI-generated podcast links for my books: 1) Lessons from AlphaZero ... 2) Parallel and Distributed Computation See web.mit.edu/dimitrib/www/b… for PDF copies #reinforcementlearning#machinelearning