Created by W.Langdon from gp-bibliography.bib Revision:1.4020

@PhdThesis{Hoock:thesis, author = "Jean-Baptiste Hoock", title = "Contributions to Simulation-based High-dimensional Sequential Decision Making", school = "Universit{\'e} Paris Sud - Paris XI", address = "France", year = "2013", month = apr # "~10", keywords = "genetic algorithms, genetic programming, GP, computer science/other, informatique/autre, Monte Carlo tree search, learning from simulations, high-dimensional sequential decision making, games, planning, Markov decision process, MoGo, MASH", bibsource = "OAI-PMH server at hal.archives-ouvertes.fr", identifier = "2013PA112053", language = "ENG", oai = "oai:tel.archives-ouvertes.fr:tel-00912338", URL = "http://tel.archives-ouvertes.fr/tel-00912338", URL = "http://tel.archives-ouvertes.fr/docs/00/91/23/38/PDF/VA2_HOOCK_Jean-Baptitste_10042013_SYNTHESE_ANNEXE.pdf", URL = "http://tel.archives-ouvertes.fr/docs/00/91/23/38/PDF/VA2_HOOCK_Jean-Baptiste_10042013.pdf", size = "238 pages", abstract = "My thesis is entitled Contributions to Simulation-based High-dimensional Sequential Decision Making. The context of the thesis is about games, planning and Markov Decision Processes. An agent interacts with its environment by successively making decisions. The agent starts from an initial state until a final state in which the agent can not make decision anymore. At each time-step, the agent receives an observation of the state of the environment. From this observation and its knowledge, the agent makes a decision which modifies the state of the environment. Then, the agent receives a reward and a new observation. The goal is to maximise the sum of rewards obtained during a simulation from an initial state to a final state. The policy of the agent is the function which, from the history of observations, returns a decision. We work in a context where (i) the number of states is huge, (ii) reward carries little information, (iii) the probability to reach quickly a good final state is weak and (iv) prior knowledge is either nonexistent or hardly exploitable. Both applications described in this thesis present these constraints : the game of Go and a 3D simulator of the European project MASH (Massive Sets of Heuristics). In order to take a satisfying decision in this context, several solutions are brought : 1. Simulating with the compromise exploration/exploitation (MCTS) 2. Reducing the complexity by local solving (GoldenEye) 3. Building a policy which improves itself (RBGP) 4. Learning prior knowledge (CluVo+GMCTS) Monte-Carlo Tree Search (MCTS) is the state of the art for the game of Go. From a model of the environment, MCTS builds incrementally and asymmetrically a tree of possible futures by performing Monte-Carlo simulations. The tree starts from the current observation of the agent. The agent switches between the exploration of the model and the exploitation of decisions which statistically give a good cumulative reward. We discuss 2 ways for improving MCTS : the parallelisation and the addition of prior knowledge. The parallelisation does not solve some weaknesses of MCTS; in particular some local problems remain challenges. We propose an algorithm (GoldenEye) which is composed of 2 parts : detection of a local problem and then its resolution. The algorithm of resolution reuses some concepts of MCTS and it solves difficult problems of a classical database. The addition of prior knowledge by hand is laborious and boring. We propose a method called Racing-based Genetic Programming (RBGP) in order to add automatically prior knowledge. The strong point is that RBGP rigorously validates the addition of a prior knowledge and RBGP can be used for building a policy (instead of only optimising an algorithm). In some applications such as MASH, simulations are too expensive in time and there is no prior knowledge and no model of the environment; therefore Monte-Carlo Tree Search can not be used. So that MCTS becomes usable in this context, we propose a method for learning prior knowledge (CluVo). Then we use pieces of prior knowledge for improving the rapidity of learning of the agent and for building a model, too. We use from this model an adapted version of Monte-Carlo Tree Search (GMCTS). This method solves difficult problems of MASH and gives good results in an application to a word game.", notes = "p116 noisy GP progress rate. Hoeffding and Bernstein bounds", }

Genetic Programming entries for Jean-Baptiste Hoock