Planification Optimiste dans les Processus Décisionnels de Markov avec Croyance

Cet article décrit l'algorithme BOP (de l'anglais ``Bayesian Optimistic Planning''), un nouvel algorithme d'apprentissage par renforcement Bayésien indirect (c'est à dire fondé sur un modèle). BOP étend l'approche de l'algorithme OP-MDP (de l'anglais ``Optimistic Planning for Markov Decision Processes'', voir [Busoniu2011,Busoniu2012]) au cas où les probabilités de transitions du MDP sous-jacent sont initialement inconnues, et doivent être apprises au travers d'interactions avec l'environnement. Les connaissances sur le MDP sous-jacent sont représentées par une distribution de probabilités sur l'ensemble de tous les modèles de transitions à l'aide de distributions de Dirichlet. L'algorithme BOP planifie dans l'espace augmenté état-croyance obtenu par concaténation du vecteur d'état avec la distribution postérieure sur les modèles de transitions. On montre que BOP atteint l'optimalité Bayésienne lorsque le paramètre de budget tend vers l'infini. Quelques expériences préliminaires montrent des résultats encourageants.Peer reviewe

Optimistic Planning for Markov Decision Processes

International audienceThe reinforcement learning community has recently intensified its interest in online planning methods, due to their relative independence on the state space size. However, tight near-optimality guarantees are not yet available for the general case of stochastic Markov decision processes and closed-loop, state-dependent planning policies. We therefore consider an algorithm related to AO* that optimistically explores a tree representation of the space of closed-loop policies, and we analyze the near-optimality of the action it returns after n tree node expansions. While this optimistic planning requires a finite number of actions and possible next states for each transition, its asymptotic performance does not depend directly on these numbers, but only on the subset of nodes that significantly impact near-optimal policies. We characterize this set by introducing a novel measure of problem complexity, called the near-optimality exponent. Specializing the exponent and performance bound for some interesting classes of MDPs illustrates the algorithm works better when there are fewer near-optimal policies and less uniform transition probabilities

Optimistic planning for continuous–action deterministic systems.

Abstract : We consider the optimal control of systems with deterministic dynamics, continuous, possibly large-scale state spaces, and continuous, low-dimensional action spaces. We describe an online planning algorithm called SOOP, which like other algorithms in its class has no direct dependence on the state space structure. Unlike previous algorithms, SOOP explores the true solution space, consisting of infinite sequences of continuous actions, without requiring knowledge about the smoothness of the system. To this end, it borrows the principle of the simultaneous optimistic optimization method, and develops a nontrivial adaptation of this principle to the planning problem. Experiments on four problems show SOOP reliably ranks among the best algorithms, fully dominating competing methods when the problem requires both long horizons and fine discretization

Combinando Modelos de Interação para Melhorar a Coordenação em Sistemas Multiagente

A contribuição principal deste artigo é a implementação de um método híbrido de coordenação a partir da combinação de modelos de interação desenvolvidos anteriormente. Os modelos de interação são baseados no compartilhamento de recompensas para aprendizagem com múltiplos agentes, no intuito de descobrir de maneira interativa políticas de boa qualidade. A troca de recompensas entre os agentes durante a interação é uma tarefa complexa e se realizada de forma inadequada pode ocasionar atrasos no aprendizado ou até mesmo causar comportamentos inesperados, tornando a cooperação ineficiente e convergindo para uma política não-satisfatória. A partir desses conceitos, o método híbrido utiliza as particularidades de cada modelo, reduzindo possíveis conflitos entre ações com recompensas de políticas diferentes, melhorando a coordenação dos agentes em problemas de aprendizagem por reforço. Resultados experimentais mostram que o método híbrido é capaz de acelerar a convergência, conquistando rapidamente políticas ótimas mesmo em grandes espaços de estados, superando os resultados de abordagens clássicas de aprendizagem por reforço

A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work