Prediction Model Using Reinforcement Deep Learning Technique for Osteoarthritis Disease Diagnosis

Osteoarthritis is the most common class of arthritis that involves tears down the soft cartilage between the joints of the knee.The regeneration of this cartilage tissue is not possible,and thus physicians typically suggest therapeutic measures to prevent further deterioration over time.Normally,bringing about joint replacement is a remedial course of action.Expose itself in joint pain recog-nized with a normal X-ray.Deep learning plays a vital role in predicting the early stages of osteoarthritis by using the MRI pictures of muscles of the knee muscle.It can be used to accurately measure the shape and texture of biological structures can be measured consistently from X-ray images.Moreover,deep learning-based computation can be used to design framework to predict whether a given patient will develop osteoarthritis.Such a framework can identify clear biochemical changes in the focal point of ligaments of the knees of patients who have exhibit pre-indications in standard imaging.This study proposes framework to identify cases of osteoarthritis by using deep learning and reinforcement learning.It can be used as a clinical mechanism to predict the occurrence of osteoarthritis so that patients can benefit from early intervention.

Reinforcement Learning from Algorithm Model to Industry Innovation Innovation: A Foundation Stone of Future Artificial Intelligence

Reinforcement learning(RL)algorithm has been introduced for several decades,which becomes a paradigm in sequential decision-making and control.The development of reinforcement learning,especially in recent years,has enabled this algorithm to be applied in many industry fields,such as robotics,medical intelligence,and games.This paper first introduces the history and background of reinforcement learning,and then illustrates the industrial application and open source platforms.After that,the successful applications from AlphaGo to AlphaZero and future reinforcement learning technique are focused on.Finally,the artificial intelligence for complex interaction(e.g.,stochastic environment,multiple players,selfish behavior,and distributes optimization)is considered and this paper concludes with the highlight and outlook of future general artificial intelligence.

Discrete-time dynamic graphical games:model-free reinforcement learning solution

This paper introduces a model-free reinforcement learning technique that is used to solve a class of dynamic games known as dynamic graphical games. The graphical game results from to make all the agents synchronize to the state of a command multi-agent dynamical systems, where pinning control is used generator or a leader agent. Novel coupled Bellman equations and Hamiltonian functions are developed for the dynamic graphical games. The Hamiltonian mechanics are used to derive the necessary conditions for optimality. The solution for the dynamic graphical game is given in terms of the solution to a set of coupled Hamilton-Jacobi-Bellman equations developed herein. Nash equilibrium solution for the graphical game is given in terms of the solution to the underlying coupled Hamilton-Jacobi-Bellman equations. An online model-free policy iteration algorithm is developed to learn the Nash solution for the dynamic graphical game. This algorithm does not require any knowledge of the agents＇ dynamics. A proof of convergence for this multi-agent learning algorithm is given under mild assumption about the inter-connectivity properties of the graph. A gradient descent technique with critic network structures is used to implement the policy iteration algorithm to solve the graphical game online in real-time.