Evaluation of Susceptibility by Mass Movements through Stochastic and Statistical Methods for a Region of Bucaramanga, Colombia

Mass movements are one of the hydrometeorological phenomena with the most negative impacts on the study area, and their evaluation through the calculation of susceptibility provides a tool of vital importance within territorial planning and disaster risk management on natural and anthropic environments. Their evaluation took algorithms designed within stochastic and statistical methods, such as the artificial neural network, the bivariate statistical method, and the logistic regression method, which integrate inherent variables (geoenvironmental characterization) against events or dependent variables. This correlation simulates regions with a probability of occurrence of mass movement under training or weight assignment. Its construction for this study took, as a basis, 50% of the events (test) and 50% of the events (validation) randomly and with equivalent area distribution against the inherent variables. As a result, it was observed that the bivariate method presented a good performance in spatial prediction. This model presents values of AUC = 82.2% (test) and AUC = 76.9% (validation), grouping a total of 591 events of the 856 events in the high category (69%). In turn, from a second evaluation carried out by this method to each hydrographic basin, a condition was established in the area (50 km2) for coherent results at a level of analysis 1:25,000, based on the idea that the variables do not present changes greater than 20% in their attributes, added to a knowledge of the area evaluated

Learning to Act with RVRL Agents

The use of reinforcement learning to guide action selection of cognitive agents has been shown to be a powerful technique for stochastic environments. Standard Reinforcement learning techniques used to provide decision theoretic policies rely, however, on explicit state-based computations of value for each state-action pair. This requires the computation of a number of values exponential to the number of state variables and actions in the system. This research extends existing work with an acquired probabilistic rule representation of an agent environment by developing an algorithm to apply reinforcement learning to values attached to the rules themselves. Structure captured by the rules is then used to learn a policy directly. The resulting value attached to each rule represents the utility of taking an action if the conditions of the rule are present in the agent’s current set of percepts. This has several advantages for planning purposes: generalization over many states and over unseen states; effective decisions can therefore be made with less training data than state based modelling systems (e.g. Dyna Q-Learning); and the problem of computation in an exponential state-action space is alleviated. The results of application of this algorithm to rules in a specific environment are presented, with comparison to standard reinforcement learning policies developed from related work

Deep Visual Foresight for Planning Robot Motion

A key challenge in scaling up robot learning to many skills and environments is removing the need for human supervision, so that robots can collect their own data and improve their own performance without being limited by the cost of requesting human feedback. Model-based reinforcement learning holds the promise of enabling an agent to learn to predict the effects of its actions, which could provide flexible predictive models for a wide range of tasks and environments, without detailed human supervision. We develop a method for combining deep action-conditioned video prediction models with model-predictive control that uses entirely unlabeled training data. Our approach does not require a calibrated camera, an instrumented training set-up, nor precise sensing and actuation. Our results show that our method enables a real robot to perform nonprehensile manipulation -- pushing objects -- and can handle novel objects not seen during training.Comment: ICRA 2017. Supplementary video: https://sites.google.com/site/robotforesight