NUST Institutions Library Catalogue Search for 'kw,wrdl: (su-rl:"Algorithms.")'

Mobile robotics: mathematics, models and methods

By Kelly, Alonzo.. . xiv, 701 pages : 27 cm. 9781107031159 (hardback) | 110703115X (hardback)

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Evolutionary computation in gene regulatory network research

. xvii, 439 pages : 24 cm.. 9781118911518 (hardback)

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Evolutionary computation in gene regulatory network research /

. xvii, 439 pages : 24 cm.. 9781118911518 (hardback)

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Linear Programming : An introduction to Finite Improvement Algorithms

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Big data systems /

By Shamsi, Jawwad Ahmad,. . pages cm 9781498752701

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Traffic Signal Control using Reinforcement Learning /

By Umer Jamil, Qazi . . 90p. ; 30cm..

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An Unreal Engine Based Human Robot Interaction Framework /

By Asif, Muhammad Hassaan . . 64p. , Existing frameworks lack the support for testing Human Robot Interaction (HRI) research which in turn often has to be tested practically making it time consuming and expensive. To overcome this issue, a HRI framework based on Unreal Engine is proposed which consists of a virtual Nao or Pepper robot along with virtual humans with verbal and non-verbal behaviours in an environment. Machine Learning (ML) algorithms along with the behaviour of the virtual robot in response to the interaction with the virtual humans and the environment can be programmed using a Python API which communicates with Unreal Engine C++ in real time. Several experiments related to multiple aspects of HRI: (1) Verbal Interaction; (2) Non-Verbal Interaction; (3) Emotional Interaction were conducted in both the virtual and real world environments and the results were compared to validate the feasibility of the framework. A Reinforcement Learning (RL) algorithm was also tested to further indicate the usefulness of the framework. Through the use of a Virtual Reality (VR) headset, a human can be immersed in the framework to interact with the robot in real time 30cm,.

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Reinforcement learning : an introduction

By Sutton, Richard S.,. . xxii, 526 pages : 24 cm.. 9780262039246 (hardcover : alk. paper)

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Control of Flywheel Inverted Pendulum Using Reinforcement Learning /

By Ahmad, Shakeel . . 67p. , Balancing an inverted pendulum is a classic control problem that traditionally requires precise system modeling for effective controller design. Reinforcement Learning (RL) offers a model-free alternative but requires extensive training, which is impractical and risky when performed directly on physical hardware. Existing methods typically rely on simulation environments built on accurate models, which are often difficult to obtain. In this work, we use RL to balance flywheel inverted pendulum by constructing an approximate model of the system through parameter estimation. Despite its inaccuracies, the model proved sufficient for training RL agents in simulation. We developed a simulation environment based on the estimated model and trained agents using Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Discrete Soft Actor-Critic (SAC) algorithms. The trained policies were deployed on real hardware without any additional fine-tuning. All agents achieved successful swing-up and stabilization, with SAC achieving the fastest swing-up time (1.65 s) and lowest steady-state error (0.0220 rad), demonstrating that RL can tolerate model imperfections and still perform effectively on real systems. 30cm.

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