We propose a modular reinforcement learning algorithm which decomposes a Markov decision process into independent modules. Each module is trained using Sarsa(λ). We introduce three algorithms for forming global policy from modules policies, and demonstrate our results using a 2D grid world.

We present and solve a real-world problem of learning to drive a bicycle. We solve the problem by online reinforcement learning using the Sarsa( )-algorithm. Then we solve the composite problem of learning to balance a bicycle and then drive to a goal. In our approach the reinforcement function is independent of the task the agent tries to learn to solve.

Non-orthogonal multiple access (NOMA) exploits the potential of power domain to enhance connectivity for Internet Things (IoT). Due time-varying communication channels, dynamic user clustering is a promising method increase throughput NOMA-IoT networks. This article develops an intelligent resource allocation scheme uplink communications. To maximise average performance sum rates, this work des...

Smart health systems typically integrate sensor technology with the Internet of Things, enabling healthcare to monitor patients. These biomedical applications collect data through remote sensors and transfer a centralized system for analysis. However, communication between edge node mobile user is susceptible impersonation attacks in computing (MEC) application. For this purpose, we propose det...

Oxidative stress is defined as a condition in which the balance between production of reactive oxygen species (ROS) and antioxidant defense system gets disturbed causes free radical induced diseases. The current research was aimed to carrying out quantification phytochemicals different parts explores activities ethanolic extracts leaf (CFL), seed (CFS), bark (CFB) show their therapeutic importa...