A Neural Networks Committee for the Contextual Bandit Problem

This paper presents a new contextual bandit algorithm, NeuralBandit, which does not need hypothesis on stationarity of contexts and rewards. Several neural networks are trained to modelize the value of rewards knowing the context. Two variants, based on multi-experts approach, are proposed to choose online the parameters of multi-layer perceptrons. The proposed algorithms are successfully tested on a large dataset with and without stationarity of rewards.Comment: 21st International Conference on Neural Information Processin

Bootstrapping Monte Carlo Tree Search with an Imperfect Heuristic

We consider the problem of using a heuristic policy to improve the value approximation by the Upper Confidence Bound applied in Trees (UCT) algorithm in non-adversarial settings such as planning with large-state space Markov Decision Processes. Current improvements to UCT focus on either changing the action selection formula at the internal nodes or the rollout policy at the leaf nodes of the search tree. In this work, we propose to add an auxiliary arm to each of the internal nodes, and always use the heuristic policy to roll out simulations at the auxiliary arms. The method aims to get fast convergence to optimal values at states where the heuristic policy is optimal, while retaining similar approximation as the original UCT in other states. We show that bootstrapping with the proposed method in the new algorithm, UCT-Aux, performs better compared to the original UCT algorithm and its variants in two benchmark experiment settings. We also examine conditions under which UCT-Aux works well.Comment: 16 pages, accepted for presentation at ECML'1

Hi-Val: Iterative Learning of Hierarchical Value Functions for Policy Generation

Task decomposition is effective in manifold applications where the global complexity of a problem makes planning and decision-making too demanding. This is true, for example, in high-dimensional robotics domains, where (1) unpredictabilities and modeling limitations typically prevent the manual specification of robust behaviors, and (2) learning an action policy is challenging due to the curse of dimensionality. In this work, we borrow the concept of Hierarchical Task Networks (HTNs) to decompose the learning procedure, and we exploit Upper Confidence Tree (UCT) search to introduce HOP, a novel iterative algorithm for hierarchical optimistic planning with learned value functions. To obtain better generalization and generate policies, HOP simultaneously learns and uses action values. These are used to formalize constraints within the search space and to reduce the dimensionality of the problem. We evaluate our algorithm both on a fetching task using a simulated 7-DOF KUKA light weight arm and, on a pick and delivery task with a Pioneer robot

Fungos endofíticos associados a acículas de Pinus taeda.

O presente trabalho objetivou estudar os fungos endofíticos em acículas de árvores jovens de Pinus taeda L. e avaliar o efeito da posição de coleta na árvore. As amostras foram coletadas em duas alturas (30-50 cm e 100-130 cm acima do solo) e nas quatro posições cardeais (norte, sul, leste e oeste), em plantas com 18 meses de idade, localizadas em Colombo, PR, Brasil. As acículas foram submetidas a assepsia e fragmentos com 10 mm de comprimento foram plaqueados em meio BDA e incubados a 28 °C, sob fotofase de 12 h, por 15 dias. Para a identificação, as estruturas reprodutivas dos fungos foram produzidas pelo método do microcultivo. Foram isolados e identificados dezessete gêneros: Alternaria, Aspergillus, Cladosporium, Colletotrichum, Coniothyrium, Diplodia, Drechslera, Hansfordia, Monocillium, Nodulisporium, Panidio, Papulaspora, Pestalotiopsis, Phialophora, Pithomyces, Rhizoctonia e Xylaria Alguns morfotipos sem identificação foram Mycelia sterilia e fungos demaciáceos. O número de isolados da altura 30-50 cm foi significativamente maior que na outra altura. Não foi observada diferença significativa no número de isolados entre as posições cardeais de uma mesma altura. Diferenças significativas foram observadas entre os gêneros isolados e Xylaria foi o gênero mais frequente

Avaliação da gomose da acácia-negra em materiais genéticos de diferentes procedências.

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