Reinforcement Learning through Supervision for Autonomous Agents

Abstract Reinforcement Learning (RL) is a class of model-free learning control methods that can solve Markov Decision Process (MDP) problems. However, one difficulty for the application of RL control is its slow convergence, especially in MDPs with continuous state space. In this paper, a modified structure of RL is proposed to accelerate reinforcement learning control. This approach combines supervision technique with the standard Qlearning algorithm of reinforcement learning. The a priori information is provided to the RL learning agent by a direct integration of a human operator commands (a.k.a. human advices) or by an optimal LQ-controller, indicating preferred actions in some particular situations. It is shown that the convergence speed of the supervised RL agent is greatly improved compared to the conventional Q-Learning algorithm. Simulation work and results on the cart-pole balancing problem and learning navigation tasks in unknown grid world with obstacles are given to illustrate the efficiency of the proposed method

A Neural Approach for the Offline Recognition of the Arabic Handwritten Words of the Algerian Departments

International audienceIn the context of the handwriting recognition, we propose an off line system for the recognition of the Arabic handwritten words of the Algerian departments. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the handwritten word by several methods. The Distribution parameters, the centered moments of the different projections of the different segments, the centered moments of the word image coding according to the directions of Freeman, and the Barr features applied binary image of the word and on its different segments. The classification is achieved by a multi layers perceptron. A detailed experiment is carried and satisfactory recognition results are reported

© 2007 Science Publications Segmentation and Recognition of Handwritten Numeric Chains

Abstract: Automatic reading of numeric chains has been attempted in several application areas such as bank cheque processing, postal code recognition and form processing. Such applications have been very popular in handwriting recognition research, due to the possibility to reduce considerably the manual effort involved in these tasks. In this study we propose an off line system for the recognition of the handwritten numeric chains. Firstly, study was based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. Used parameters to form the input vector of the neural network are extracted on the binary images of the digits by several methods: distribution sequence, Barr features and centred moments of different projections and profiles. Secondly, study was extented for the reading of the handwritten numeric chains constituted of a variable number of digits. Vertical projection was used to segment the numeric chain at isolated digits and every digit (or segment) was presented separately to the entry of the system achieved in the first part (recognition system of the isolated handwritten digits). The performances of the proposed system for the used database attain a recognition rate equal to 91.3%