Purpose -This purpose of this paper is to provide an overview of the theoretical background and applications of inverse reinforcement learning (IRL). Design/methodology/approach -Reinforcement learning (RL) techniques provide a powerful solution for sequential decision making problems under uncertainty. RL uses an agent equipped with a reward function to find a policy through interactions with a dynamic environment. However, one major assumption of existing RL algorithms is that reward function, the most succinct representation of the designer's intention, needs to be provided beforehand. In practice, the reward function can be very hard to specify and exhaustive to tune for large and complex problems, and this inspires the development of IRL, an extension of RL, which directly tackles this problem by learning the reward function through expert demonstrations. In this paper, the original IRL algorithms and its close variants, as well as their recent advances are reviewed and compared. Findings -This paper can serve as an introduction guide of fundamental theory and developments, as well as the applications of IRL. Originality/value -This paper surveys the theories and applications of IRL, which is the latest development of RL and has not been done so far.

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High Speed Machining centers (HSM) are considered as complicated industrial instruments. Finishing is a critical process in production procedure which is carried on by these machines. Among many types of cutters, ball-nose cutters are the preferred cutters to do these kinds of operations since they have extensive operating cutting edges and appropriate geometry. The main aim of the researches on cutting process is to understand its nature better and to use this knowledge to improve the quality of the product. To achieve this goal, it is necessary to have a descriptive reference model on the process using experiments' data. Increasing demands for better surfacefinishing and concurrently the development of the available measurement instruments and modeling techniques make the methods and approaches to be novel. Present paper is a survey on the lack of literature on the state-of-the-art modeling paradigms of milling processes, mainly on ball-nose cutters for surface finishing.

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Abstract-this paper discusses extended approaches of Adaptive Network Based Fuzzy Inference System (ANFIS) using Self Tuning Regulator (STR) via Fuzzy logic to be implemented in cascade control. Plant used is Pressure RIG 38-714 which supports cascade configuration. The controlled variable in the outer loop is pressure, in the inner loop's flow. The proposed method's used to improve the performance of ANFIS which has been commonly implemented to accomplish control task. Comparison has been conducted between ANFIS using STR and without STR, from experiment could be concluded that controller ANFIS using STR acquired better performance than only used ANFIS. It's derived from the transient response of those. For ANFIS using STR is obtained rise time and settling time are 9 ms and 12 ms respectively. In the other hand, ANFIS without STR resulted 21 ms and 29 ms for rise time and settling time respectively.