PPT-Batch RL Via Least Squares Policy Iteration

Alan Fern Based in part on slides by Ronald Parr Overview Motivation LSPI Derivation from LSTD Experimental results Online versus Batch RL Online RL integrates data collection and optimization. Sum & Difference of Two Cubes. Recognizing Perfect Squares . Difference of Two Squares. Recognizing Perfect Cubes. Sum of Two Cubes. Difference of Two Cubes. 5.6. 1. Recognizing Perfect Squares (X). Least Squares. Method. of . Least. . Squares. :. Deterministic. . approach. . The. . inputs. u(1), u(2), ..., u(N) . are. . applied. . to. . the. . system. The. . outputs. y(1), y(2), ..., y(N) . Adaptive Filters. Definition. With the arrival of new data samples estimates are updated recursively.. Introduce a weighting factor to the sum-of-error-squares definition. Weighting factor. Forgetting factor. Chapter 3 – Exploring Data. Day 3. Regression Line. A straight line that describes how a . _________ . variable, . __. ,. . changes as an . ___________ variable. , . ___. ,. . changes. used to . __________ . Reinforcement Learning. . Alan Fern. . * Based in part on slides by Ronald Parr. Overview. What is batch reinforcement learning? . Least Squares Policy Iteration. Fitted Q-iteration. Batch DQN. Online versus Batch RL. Frank Lin. 10-710 Structured Prediction. School of Computer Science. Carnegie Mellon . University. 2011-11-28. Talk Outline. Clustering. Spectral Clustering. Power Iteration Clustering (PIC). PIC with Path Folding. b. -values for Three Different Tectonic Regimes. Christine . Gammans. What is the . b. -value and why do we care?. Earthquake occurrence per magnitude follows a power law introduced by Ishimoto and Iida (1939) and Guten. …or . Things They Wouldn’t Do to the Music Teacher. By Sean Marsh. 28 Years on 64 Squares. 1988. . 28 Years on 64 Squares. 1988: Early Days…. 28 Years on 64 Squares. How we expect a school to be:. EUROGRAPHICS 2005. Presenter : . Jong. -Hyun Kim. Abstract. We present a new method for surface extraction from volume data.. Maintains consistent topology and generates surface adaptively without . crack . Data. Model with only main . effects (JMP output): . Center. . Level Least Sq Mean . Mean. . 1 4.00 4.00 . 2 6.00 6.00 . Knit squares of any size are stitched into bunnies by children fighting cancer in “Critter Creation Kits.” Some squares are made into bunnies by expert critter makers for younger children to snuggle.. scalability . improvements . and . applications . to . difference . of convex programming.. Georgina . Hall. Princeton, . ORFE. Joint work with . Amir Ali Ahmadi. Princeton, ORFE. 1. Nonnegative polynomials. ANOVA Terms — Sums of Squares. S.S.—The sum of squared deviations of each data point from some mean value. Between groups—The difference between S.S. combined and S.S. within . groups. [variability due to IV]. Paige Thielen, ME535 Spring 2018. Abstract. Various methods of accelerometer calibration can be used to increase the precision of acceleration measurements. The methods tested are two 12-parameter linear least squares optimizations, one using four calibration orientations, one using eight orientations, and two 15-parameter least squares optimizations using eight and 19 calibration orientations. Based on the data gathered, while it is not necessary to change the calibration method currently in use, good results could be obtained from applying a 12-parameter, 8-orientation least squares calibration without significant increase in time required for calibration..