2,763 research outputs found
MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning
Reinforcement learning has become one of the best approach to train a
computer game emulator capable of human level performance. In a reinforcement
learning approach, an optimal value function is learned across a set of
actions, or decisions, that leads to a set of states giving different rewards,
with the objective to maximize the overall reward. A policy assigns to each
state-action pairs an expected return. We call an optimal policy a policy for
which the value function is optimal. QLBS, Q-Learner in the
Black-Scholes(-Merton) Worlds, applies the reinforcement learning concepts, and
noticeably, the popular Q-learning algorithm, to the financial stochastic model
of Black, Scholes and Merton. It is, however, specifically optimized for the
geometric Brownian motion and the vanilla options. Its range of application is,
therefore, limited to vanilla option pricing within financial markets. We
propose MQLV, Modified Q-Learner for the Vasicek model, a new reinforcement
learning approach that determines the optimal policy of money management based
on the aggregated financial transactions of the clients. It unlocks new
frontiers to establish personalized credit card limits or to fulfill bank loan
applications, targeting the retail banking industry. MQLV extends the
simulation to mean reverting stochastic diffusion processes and it uses a
digital function, a Heaviside step function expressed in its discrete form, to
estimate the probability of a future event such as a payment default. In our
experiments, we first show the similarities between a set of historical
financial transactions and Vasicek generated transactions and, then, we
underline the potential of MQLV on generated Monte Carlo simulations. Finally,
MQLV is the first Q-learning Vasicek-based methodology addressing transparent
decision making processes in retail banking
Visualization of AE's Training on Credit Card Transactions with Persistent Homology
Auto-encoders are among the most popular neural network architecture for
dimension reduction. They are composed of two parts: the encoder which maps the
model distribution to a latent manifold and the decoder which maps the latent
manifold to a reconstructed distribution. However, auto-encoders are known to
provoke chaotically scattered data distribution in the latent manifold
resulting in an incomplete reconstructed distribution. Current distance
measures fail to detect this problem because they are not able to acknowledge
the shape of the data manifolds, i.e. their topological features, and the scale
at which the manifolds should be analyzed. We propose Persistent Homology for
Wasserstein Auto-Encoders, called PHom-WAE, a new methodology to assess and
measure the data distribution of a generative model. PHom-WAE minimizes the
Wasserstein distance between the true distribution and the reconstructed
distribution and uses persistent homology, the study of the topological
features of a space at different spatial resolutions, to compare the nature of
the latent manifold and the reconstructed distribution. Our experiments
underline the potential of persistent homology for Wasserstein Auto-Encoders in
comparison to Variational Auto-Encoders, another type of generative model. The
experiments are conducted on a real-world data set particularly challenging for
traditional distance measures and auto-encoders. PHom-WAE is the first
methodology to propose a topological distance measure, the bottleneck distance,
for Wasserstein Auto-Encoders used to compare decoded samples of high quality
in the context of credit card transactions.Comment: arXiv admin note: substantial text overlap with arXiv:1905.0989
Alteration and release of aliphatic compounds by the polychaete Nereis virens (Sars) experimentally fed with hydrocarbons
In the laboratory, marine worms were fed with a mixture of algae and several aliphatic hydrocarbons for 15 days. After ingestion by the worms, 34.9% of hydrocarbons are found in the faeces and only 3.1% accumulated in the gut. The comparison between the initial mixture and the faeces shows that the worm’s digestive process lead to changes in the distribution of the n-alkane mixture. These changes are different from those only due to physical processes in the experimental conditions. In our experiment, no variation in the distribution of hydrocarbons in faeces with time and no microbial hydrocarbon biodegradation were evidenced. Our results suggest that marine worm feeding can substantially affect the fate of hydrocarbons in the sedimentary marine
ecosystem by predominantly stimulating dissolution processes
Pharmaceutische Waarenkunde
Die Waarenkunden steht in vielfacher Beziehung mit der Naturgeschichte, darf aber nicht ganz mit derselben verwechselt werden. ..
Effects of temperature on in vitro sediment reworking processes by a gallery biodiffusor, the polychaete Neanthes virens
Temperature-induced variations in bioturbation could affect sediment mixing processes in the marine benthic environment. In this study, sediment reworking by Neanthes virens (Sars), a widely distributed polychaete in muddy sand communities of northern temperate latitudes, was studied under different temperature conditions representing winter (1°C), spring and fall (6°C), summer(13°C), and tide pool (18°C) temperatures in the lower St. Lawrence Estuary, Québec, Canada. Sediment reworking was quantified using inert fluorescent particles (luminophores) deposited at the sediment surface. Based on the 1-D luminophore distributions obtained after 5 and 30 d, the use of the
specific ‘gallery-biodiffusor’ model allowed us to quantify both biodiffusion (Db) and biotransport (Vb) due to the organisms. Our results showed temperature effects on sediment transport. The lowest biotransport and biodiffusion coefficients were measured at 1 and 6°C and did not change with time. The highest biodiffusion occurred at 13°C for both sampling periods. At 18°C, biodiffusion was intermediate while biotransport was maximal. Differences between the 13°C biodiffusive transport and
the other temperatures increased with time. Low transport values at 1 and 6°C suggest that a quiescent stage exists for this species at these temperatures, with sediment mixing occurring mostly during burrow construction. On the other hand, sediment mixing resulted from both the burrow
construction and maintenance phases at higher temperatures (13 and 18°C)
Groupes ethniques et foyers potentiels de fièvre jaune dans les Etats francophones d'Afrique occidentale : considérations sur les méthodes de lutte contre Aedes aegypti
SALT "Savanes à Long Terme" : site de Thyssé-Kaymor, Sénégal : rapport de la campagne de mesures hydrologiques sur le bassin versant de Sonkorong : année 1993
Monte Carlo Tree Search Guided by Symbolic Advice for MDPs
In this paper, we consider the online computation of a strategy that aims at optimizing the expected average reward in a Markov decision process. The strategy is computed with a receding horizon and using Monte Carlo tree search (MCTS). We augment the MCTS algorithm with the notion of symbolic advice, and show that its classical theoretical guarantees are maintained. Symbolic advice are used to bias the selection and simulation strategies of MCTS. We describe how to use QBF and SAT solvers to implement symbolic advice in an efficient way. We illustrate our new algorithm using the popular game Pac-Man and show that the performances of our algorithm exceed those of plain MCTS as well as the performances of human players
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