Transforming an infinite horizon problem into a Dynamic Programming one
This video shows how to transform an infinite horizon optimization problem into a dynamic programming one. The Bellman equation or value function is ...
Constantin Bürgi
Lecture 5: Search 1 - Dynamic Programming, Uniform Cost Search | Stanford CS221: AI (Autumn 2019)
Topics: Problem-solving as finding paths in graphs, Tree search, Dynamic programming, uniform cost search Percy Liang, Associate Professor & Dorsa Sadigh, ...
stanfordonline
Reinforcement Learning: Hidden Theory and New Super-Fast Algorithms
Sean Meyn, University of Florida https://simons.berkeley.edu/events/reinforcement-learning-hidden-theory-and-new-super-fast-algorithms.
Simons Institute
Approximate Dynamic Learning - Dimitri P. Bertsekas (Lecture 1, Part A)
Prof. Bertsekas at the KIOS Distinguished Lecture Series On the 18th of September 2017, the KIOS Research and Innovation Center of Excellence (CoE) ...
KIOS CoE
19. Dynamic Programming I: Fibonacci, Shortest Paths
MIT 6.006 Introduction to Algorithms, Fall 2011 View the complete course: http://ocw.mit.edu/6-006F11 Instructor: Erik Demaine License: Creative Commons ...
MIT OpenCourseWare
Lecture 8: Markov Decision Processes - Reinforcement Learning | Stanford CS221: AI (Autumn 2019)
Topics: Reinforcement learning, Monte Carlo, SARSA, Q-learning, Exploration/exploitation, function approximation Percy Liang, Associate Professor & Dorsa ...
stanfordonline
Lecture 10: Game Playing 2 - TD Learning, Game Theory | Stanford CS221: AI (Autumn 2019)
Topics: TD learning, Game theory Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University http://onlinehub.stanford.edu/ ...
stanfordonline
Lecture 19 (Bellman Eq.)
Learning Theory (Reza Shadmehr, PhD) Introduction to optimal feedback control. Bellman's equation.
JHU Learning Theory
Reinforcement Learning (SS20) - Lecture 3 - Dynamic Programming
Humans to Robots Motion Research Group
Grad Course in AI (#11): Markov Decision Processes
Dr. Mausam (University of Washington) teaches Markov Decision Processes: MDP models (discounting, horizons, cost/reward), solutions to MDPs including ...
Mausam
Stochastic programming, dynamic programming and their use in climate change economics
Lecture given by THOMAS RUTHERFORD, University of Wisconsin-Madison, USA This lecture provides a brief introduction to modeling tools appropriate to ...
ICCGOV
Stuart Russell: "Probabilistic programming and AI"
PROBPROG Conference
Every Optimization Problem Is a Quadratic Program:...
Sean Meyn (University of Florida) https://simons.berkeley.edu/talks/tbd-189 Theory of Reinforcement Learning Boot Camp.
Simons Institute
Markov Chain Compression (Ep 3, Compressor Head)
Markov Chains Compression sits at the cutting edge of compression algorithms. These algorithms take an Artificial Intelligence approach to compression by ...
Google Developers
RL Course by David Silver - Lecture 2: Markov Decision Process
Reinforcement Learning Course by David Silver# Lecture 2: Markov Decision Process #Slides and more info about the course: http://goo.gl/vUiyjq.
DeepMind
Richard Murray - CS+Biology - Alumni College 2016
"Synthetic Biology: Programming Cellular Behavior Using DNA" Richard Murray (BS '85), the Thomas E. and Doris Everhart Professor of Control and Dynamical ...
caltech
Lecture 1: Overview | Stanford CS221: AI (Autumn 2019)
Topics: Overview of course, Optimization Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University ...
stanfordonline
Bellman Equation Basics for Reinforcement Learning
An introduction to the Bellman Equations for Reinforcement Learning. Part of the free Move 37 Reinforcement Learning course at The School of AI.
Skowster the Geek
Intro to Dynamic Programming-1
This project was created with Explain Everything™ Interactive Whiteboard for iPad.
Tim Kearns
Stochastic Programming Approach to Optimization Under Uncertainty (Part 1)
Alex Shapiro (Georgia Tech) https://simons.berkeley.edu/talks/tbd-186 Theory of Reinforcement Learning Boot Camp.
Simons Institute
introduction to Markov Decision Processes (MFD)
This is a basic intro to MDPx and value iteration to solve them..
Francisco Iacobelli
JuliaCon 2017 | Decision Making under Uncertainty | Mykel Kochenderfer
Visit http://julialang.org/ to download Julia.
The Julia Programming Language
Graphs and Networks | MIT 18.06SC Linear Algebra, Fall 2011
Graphs and Networks Instructor: Nikola Kamburov View the complete course: http://ocw.mit.edu/18-06SCF11 License: Creative Commons BY-NC-SA More ...
MIT OpenCourseWare
Session 3. Andrzej Ruszczyński: Risk quantification and control
Title: Risk quantification and control: Challenges and opportunities Abstract: At first, we shall identify strategic research directions in modern systems ...
IIASA
MIT CompBio Lecture 05 - HMMs Hidden Markov Models II (Fall'19)
MIT Computational Biology: Genomes, Networks, Evolution, Health http://compbio.mit.edu/6.047/ Prof. Manolis Kellis Full playlist with all videos in order is here: ...
Manolis Kellis
Geometric Insights into the convergence of Non-linear TD Learning Joan Bruna
Seminar on Theoretical Machine Learning Topic: Geometric Insights into the convergence of Non-linear TD Learning Speaker: Joan Bruna Affiliation: New York ...
Institute for Advanced Study
Mehran Sahami: How can you make the best possible decisions?
How do you make decisions in your daily life? Many people make seemingly reasonable, yet ultimately irrational decisions. However, we can learn to make ...
Stanford University School of Engineering
35C3 - Information Biology - Investigating the information flow in living systems
https://media.ccc.de/v/35c3-9734-information_biology_-_investigating_the_information_flow_in_living_systems From cells to dynamic models of biochemical ...
media.ccc.de
Reinforcement Learning in the Presence of Nonstationary Variables with Simon Ouellette
Conventional reinforcement learning is difficult, perhaps impossible to use "as is" in the context of financial trading, due to the presence of time-varying ...
Quantopian
Introduction to Molecular Dynamics Simulations
This online webinar shared an introduction to Molecular Dynamics (MD) simulations as well as explored some of the basic features and capabilities of LAMMPS ...
WestGrid
CS 285: Lecture 2, Part 1
RAIL
Dimitri Bertsekas: Stable Optimal Control and Semicontractive Dynamic Programming
This distinguished lecture was originally streamed on Monday, October 23rd, 2017. The full title of this seminar is as follows: Stable Optimal Control and ...
UTC Institute for Advanced Systems Engineering
Reinforcement Learning 6: Policy Gradients and Actor Critics
Hado Van Hasselt, Research Scientist, discusses policy gradients and actor critics as part of the Advanced Deep Learning & Reinforcement Learning Lectures.
DeepMind
Advanced Machine Learning Day 3: Neural Architecture Search
How do you search over architectures? View presentation slides and more at ...
Microsoft Research
Introduction to Molecular Dynamics
SimbiosOpenMM
On The Hardness of Reinforcement Learning With Value-Function Approximation
Value-function approximation methods that operate in batch mode have foundational importance to reinforcement learning (RL). Finite sample guarantees for ...
Microsoft Research
Symposium in Honor of Robert C. Merton - Day 2: Andrew Lo
Robert C. Merton: The First Financial Engineer by Andrew Lo (MIT). Symposium in Honor of Robert C. Merton, PhD '70 August 5-6, 2019 MIT Sloan School of ...
Finance at MIT
Discussion: Optimization
Moderator: Gergely Neu (UPF) https://simons.berkeley.edu/talks/tbd-228 Deep Reinforcement Learning.
Simons Institute
Reinforcement Learning 10: Classic Games Case Study
David Silver, Research Scientist, discusses classic games as part of the Advanced Deep Learning & Reinforcement Learning Lectures.
DeepMind
Lecture 9: Game Playing 1 - Minimax, Alpha-beta Pruning | Stanford CS221: AI (Autumn 2019)
Topics: Minimax, expectimax, Evaluation functions, Alpha-beta pruning Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford ...
stanfordonline
Lecture 3: Machine Learning 2 - Features, Neural Networks | Stanford CS221: AI (Autumn 2019)
Topics: Features and non-linearity, Neural networks, nearest neighbors Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford ...
stanfordonline
[PyEMMA 2018] Introduction to Markov state models
Frank Noé gives an introduction to Markov state modelling of molecular dynamics using the PyEMMA software. PyEMMA (EMMA = Emma's Markov Model ...
MarkovModel
