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In this paper, $H_\infty $ control problem is investigated by off-policy integral reinforcement learning (IRL) method for the nonlinear systems with completely unknown dynamics, disturbances, and constrained-input. Firstly, according to a model-based policy iteration (PI) algorithm, a model-free algorithm is proposed based on the derived iterative equation, and the equivalence of model-based PI algorithm and model-free algorithm is proven. Then, the model-free algorithm is implemented by off-policy IRL technology to solve the Hamilton-Jacobi-Isaacs (HJI) equation with the collected system data by the least-square approach, where three neural networks (NNs) are constructed to approximate the value function, control and the disturbance. Finally, our proposed methods are applied to stabilize an autonomous third-order Chua’s chaotic circuit system and a non-autonomous second-order memristive chaotic circuit system to illustrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

The article highlights the first workshop on Information Retrieval Over Query Sessions in Dublin, Ireland at European Conference on Information Retrieval (ECIR) 2011. This event was organized for the investigation of the questions of measurement, analysis and optimization of information retrieval (IR) system behavior. It was structured with the theme on algorithm that uses information on query sessions for various tasks and query session detection and construction of query session collection.