Crisis, endeudamiento y desposesión en el mundo rural catalán de finales del siglo XIX

Esta tesis doctoral examina las repercusiones de la llamada "Gran Depresión" de finales del siglo XIX sobre la sociedad rural catalana a partir del análisis del que puede ser visto como uno de los síntomas más característicos de cualquier crisis agraria: la pérdida de derechos de propiedad sobre la tierra o sobre otros inmuebles de carácter rural como consecuencia de reclamaciones de deudas, promovidas por particulares o por el Estado, que culminaron en subasta pública. El trabajo ha sido dividido en dos secciones. En la primera se analiza la desposesión causada por procedimientos ejecutivos impulsados por la Administración de Hacienda por impuestos impagados. En la segunda se ha estudiado la actividad judicial que concluyó en subasta pública de bienes inmuebles, y se han mostrado sus relaciones con la situación agraria, especialmente en los sectores del trigo y de la vid.

Semi-online neural-Q_leaming for real-time robot learning

Reinforcement learning (RL) is a very suitable technique for robot learning, as it can learn in unknown environments and in real-time computation. The main difficulties in adapting classic RL algorithms to robotic systems are the generalization problem and the correct observation of the Markovian state. This paper attempts to solve the generalization problem by proposing the semi-online neural-Q_learning algorithm (SONQL). The algorithm uses the classic Q_learning technique with two modifications. First, a neural network (NN) approximates the Q_function allowing the use of continuous states and actions. Second, a database of the most representative learning samples accelerates and stabilizes the convergence. The term semi-online is referred to the fact that the algorithm uses the current but also past learning samples. However, the algorithm is able to learn in real-time while the robot is interacting with the environment. The paper shows simulated results with the "mountain-car" benchmark and, also, real results with an underwater robot in a target following behavior

Policy gradient based Reinforcement Learning for real autonomous underwater cable tracking

This paper proposes a field application of a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot in cable tracking task. The learning system is characterized by using a direct policy search method for learning the internal state/action mapping. Policy only algorithms may suffer from long convergence times when dealing with real robotics. In order to speed up the process, the learning phase has been carried out in a simulated environment and, in a second step, the policy has been transferred and tested successfully on a real robot. Future steps plan to continue the learning process on-line while on the real robot while performing the mentioned task. We demonstrate its feasibility with real experiments on the underwater robot ICTINEU AUV