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Constructing Skill Trees
Constructing skill trees (CST) is a hierarchical reinforcement learning algorithm which can build skill trees from a set of sample solution trajectories obtained from demonstration. CST uses an incremental MAP (maximum a posteriori) change point detection algorithm to segment each demonstration trajectory into skills and integrate the results into a skill tree. CST was introduced by George Konidaris, Scott Kuindersma, Andrew Barto and Roderic Grupen in 2010. Algorithm CST consists of mainly three parts;change point detection, alignment and merging. The main focus of CST is online change-point detection. The change-point detection algorithm is used to segment data into skills and uses the sum of discounted reward R_t as the target regression variable. Each skill is assigned an appropriate abstraction. A particle filter is used to control the computational complexity of CST. The change point detection algorithm is implemented as follows. The data for times t\in T and models ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Reinforcement Learning
Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input-output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge) with the goal of maximizing the cumulative reward (the feedback of which might be incomplete or delayed). The search for this balance is known as the exploration–exploitation dilemma. The environment is typically stated in the form of a Markov decision process (MDP), as many reinforcement learning algorithms use dyn ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Maximum A Posteriori
An estimation procedure that is often claimed to be part of Bayesian statistics is the maximum a posteriori (MAP) estimate of an unknown quantity, that equals the mode of the posterior density with respect to some reference measure, typically the Lebesgue measure. The MAP can be used to obtain a point estimate of an unobserved quantity on the basis of empirical data. It is closely related to the method of maximum likelihood (ML) estimation, but employs an augmented optimization objective which incorporates a prior density over the quantity one wants to estimate. MAP estimation is therefore a regularization of maximum likelihood estimation, so is not a well-defined statistic of the Bayesian posterior distribution. Description Assume that we want to estimate an unobserved population parameter \theta on the basis of observations x. Let f be the sampling distribution of x, so that f(x\mid\theta) is the probability of x when the underlying population parameter is \theta. T ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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George Konidaris
George may refer to: Names * George (given name) * George (surname) People * George (singer), American-Canadian singer George Nozuka, known by the mononym George * George Papagheorghe, also known as Jorge / GEØRGE * George, stage name of Giorgio Moroder * George, son of Andrew I of Hungary Places South Africa * George, South Africa, a city ** George Airport United States * George, Iowa, a city * George, Missouri, a ghost town * George, Washington, a city * George County, Mississippi * George Air Force Base, a former U.S. Air Force base located in California Computing * George (algebraic compiler) also known as 'Laning and Zierler system', an algebraic compiler by Laning and Zierler in 1952 * GEORGE (computer), early computer built by Argonne National Laboratory in 1957 * GEORGE (operating system), a range of operating systems (George 1–4) for the ICT 1900 range of computers in the 1960s * GEORGE (programming language), an autocode system invented by Charles Leonard Hambli ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Scott Kuindersma
Scott may refer to: Places Canada * Scott, Quebec, municipality in the Nouvelle-Beauce regional municipality in Quebec * Scott, Saskatchewan, a town in the Rural Municipality of Tramping Lake No. 380 * Rural Municipality of Scott No. 98, Saskatchewan United States * Scott, Arkansas * Scott, Georgia * Scott, Indiana * Scott, Louisiana * Scott, Missouri * Scott, New York * Scott, Ohio * Scott, Wisconsin (other) (several places) * Fort Scott, Kansas * Great Scott Township, St. Louis County, Minnesota * Scott Air Force Base, Illinois * Scott City, Kansas * Scott City, Missouri * Scott County (other) (various states) * Scott Mountain (other) (several places) * Scott River, in California * Scott Township (other) (several places) Elsewhere * 876 Scott, minor planet orbiting the Sun * Scott (crater), a lunar impact crater near the south pole of the Moon *Scott Conservation Park, a protected area in South Australia Lists * Scott Point (disamb ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Andrew Barto
Andrew Gehret Barto (born 1948 or 1949) is an American computer scientist, currently Professor Emeritus of computer science at University of Massachusetts Amherst. Barto is best known for his foundational contributions to the field of modern computational reinforcement learning. Early life and education Andrew Gehret Barto was born in either 1948 or 1949. He received his B.S. with distinction in mathematics from the University of Michigan in 1970, after having initially majored in naval architecture and engineering. After reading work by Michael Arbib, Warren Sturgis McCulloch, and Walter Pitts, he became interested in using computers and mathematics to model the brain, and five years later was awarded a Ph.D. in computer science for a thesis on cellular automata. Career In 1977, Barto joined the College of Information and Computer Sciences at the University of Massachusetts Amherst as a postdoctoral research associate, was promoted to associate professor in 1982, and full ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Roderic Grupen
Rod Grupen is a professor of Computer science and director of the Laboratory for Perceptual Robotics at the University of Massachusetts Amherst, Amherst. Grupen's research integrates signal processing, control, dynamical systems, learning, and development as a means of constructing intelligent systems. He has published over 100 peer-reviewed journal, conference, and workshop papers. Grupen is the co-editor-in-chief of the ''Robotics and Autonomous Systems Journal'' and serves on the editorial board of the ''Journal of Artificial Intelligence for Engineering Design, Analysis and Manufacturing'' (AI EDAM). In 2010, Grupen received the Chancellor's Medal, the highest honor bestowed on individuals for exemplary and extraordinary service to the University of Massachusetts. Grupen received both a B.S. in mechanical engineering from Washington University in St. Louis and a B.A. in physics from Franklin and Marshall College in 1980, an M.S. in mechanical engineering from Pennsylvania S ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Particle Filter
Particle filters, also known as sequential Monte Carlo methods, are a set of Monte Carlo algorithms used to find approximate solutions for filtering problems for nonlinear state-space systems, such as signal processing and Bayesian statistical inference. The filtering problem consists of estimating the internal states in dynamical systems when partial observations are made and random perturbations are present in the sensors as well as in the dynamical system. The objective is to compute the posterior distributions of the states of a Markov process, given the noisy and partial observations. The term "particle filters" was first coined in 1996 by Pierre Del Moral about mean-field interacting particle methods used in fluid mechanics since the beginning of the 1960s. The term "Sequential Monte Carlo" was coined by Jun S. Liu and Rong Chen in 1998. Particle filtering uses a set of particles (also called samples) to represent the posterior distribution of a stochastic process giv ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Viterbi Algorithm
The Viterbi algorithm is a dynamic programming algorithm for obtaining the maximum a posteriori probability estimate of the most likely sequence of hidden states—called the Viterbi path—that results in a sequence of observed events. This is done especially in the context of Markov information sources and hidden Markov models (HMM). The algorithm has found universal application in decoding the convolutional codes used in both CDMA and GSM digital cellular, dial-up modems, satellite, deep-space communications, and 802.11 wireless LANs. It is now also commonly used in speech recognition, speech synthesis, diarization, keyword spotting, computational linguistics, and bioinformatics. For example, in speech-to-text (speech recognition), the acoustic signal is treated as the observed sequence of events, and a string of text is considered to be the "hidden cause" of the acoustic signal. The Viterbi algorithm finds the most likely string of text given the acoustic signal. His ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Pseudocode
In computer science, pseudocode is a description of the steps in an algorithm using a mix of conventions of programming languages (like assignment operator, conditional operator, loop) with informal, usually self-explanatory, notation of actions and conditions. Although pseudocode shares features with regular programming languages, it is intended for human reading rather than machine control. Pseudocode typically omits details that are essential for machine implementation of the algorithm, meaning that pseudocode can only be verified by hand. The programming language is augmented with natural language description details, where convenient, or with compact mathematical notation. The reasons for using pseudocode are that it is easier for people to understand than conventional programming language code and that it is an efficient and environment-independent description of the key principles of an algorithm. It is commonly used in textbooks and scientific publications to document ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Skill Chaining
Skill chaining is a skill discovery method in continuous reinforcement learning Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learnin .... It has been extended to high-dimensional continuous domains by the related Deep skill chaining algorithm. References * * Machine learning algorithms {{machine-learning-stub ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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PinBall
Pinball games are a family of games in which a ball is propelled into a specially designed table where it bounces off various obstacles, scoring points either en route or when it comes to rest. Historically the board was studded with nails called 'pins' and had hollows or pockets which scored points if the ball came to rest in them. Today, pinball is most commonly an arcade game in which the ball is fired into a specially designed cabinet known as a pinball machine, hitting various lights, bumpers, ramps, and other targets depending on its design. The game's object is generally to score as many points as possible by hitting these targets and making various shots with flippers before the ball is lost. Most pinball machines use one ball per turn, except during special multi-ball phases, and the game ends when the ball(s) from the last turn are lost. The biggest pinball machine manufacturers historically include Bally Manufacturing, Gottlieb, Williams Electronics and Stern P ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Prefrontal Cortex Basal Ganglia Working Memory
Prefrontal cortex basal ganglia working memory (PBWM) is an algorithm that Computer simulation, models working memory in the prefrontal cortex and the basal ganglia. It can be compared to long short-term memory (LSTM) in functionality, but is more biologically explainable. It uses the PVLV, primary value learned value model to train prefrontal cortex working-memory updating system, based on the biology of the prefrontal cortex and basal ganglia. It is used as part of the Leabra framework and was implemented in Emergent (software), Emergent in 2019. Abstract The prefrontal cortex has long been thought to subserve both working memory (the holding of information online for processing) and "executive" functions (deciding how to manipulate working memory and perform processing). Although many computational models of working memory have been developed, the mechanistic basis of executive function remains elusive. PBWM is a computational model of the prefrontal cortex to control both its ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |