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Full-Text Articles in Engineering
An Application Of Sliding Mode Control To Model-Based Reinforcement Learning, Aaron Thomas Parisi
An Application Of Sliding Mode Control To Model-Based Reinforcement Learning, Aaron Thomas Parisi
Master's Theses
The state-of-art model-free reinforcement learning algorithms can generate admissible controls for complicated systems with no prior knowledge of the system dynamics, so long as sufficient (oftentimes millions) of samples are available from the environ- ment. On the other hand, model-based reinforcement learning approaches seek to leverage known optimal or robust control to reinforcement learning tasks by mod- elling the system dynamics and applying well established control algorithms to the system model. Sliding-mode controllers are robust to system disturbance and modelling errors, and have been widely used for high-order nonlinear system control. This thesis studies the application of sliding mode control …
Reinforcement Learning For Self Organization And Power Control Of Two-Tier Heterogeneous Networks, Roohollah Amiri, Mojtaba Ahmadi Almasi, Jeffrey G. Andrews, Hani Mehrpouyan
Reinforcement Learning For Self Organization And Power Control Of Two-Tier Heterogeneous Networks, Roohollah Amiri, Mojtaba Ahmadi Almasi, Jeffrey G. Andrews, Hani Mehrpouyan
Electrical and Computer Engineering Faculty Publications and Presentations
Self-organizing networks (SONs) can help manage the severe interference in dense heterogeneous networks (HetNets). Given their need to automatically configure power and other settings, machine learning is a promising tool for data-driven decision making in SONs. In this paper, a HetNet is modeled as a dense two-tier network with conventional macrocells overlaid with denser small cells (e.g. femto or pico cells). First, a distributed framework based on multi-agent Markov decision process is proposed that models the power optimization problem in the network. Second, we present a systematic approach for designing a reward function based on the optimization problem. Third, we …
Data-Driven Integral Reinforcement Learning For Continuous-Time Non-Zero-Sum Games, Yongliang Yang, Liming Wang, Hamidreza Modares, Dawei Ding, Yixin Yin, Donald C. Wunsch
Data-Driven Integral Reinforcement Learning For Continuous-Time Non-Zero-Sum Games, Yongliang Yang, Liming Wang, Hamidreza Modares, Dawei Ding, Yixin Yin, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
This paper develops an integral value iteration (VI) method to efficiently find online the Nash equilibrium solution of two-player non-zero-sum (NZS) differential games for linear systems with partially unknown dynamics. To guarantee the closed-loop stability about the Nash equilibrium, the explicit upper bound for the discounted factor is given. To show the efficacy of the presented online model-free solution, the integral VI method is compared with the model-based off-line policy iteration method. Moreover, the theoretical analysis of the integral VI algorithm in terms of three aspects, i.e., positive definiteness properties of the updated cost functions, the stability of the closed-loop …
Viewpoint Optimization For Autonomous Strawberry Harvesting With Deep Reinforcement Learning, Jonathon J. Sather
Viewpoint Optimization For Autonomous Strawberry Harvesting With Deep Reinforcement Learning, Jonathon J. Sather
Master's Theses
Autonomous harvesting may provide a viable solution to mounting labor pressures in the United States' strawberry industry. However, due to bottlenecks in machine perception and economic viability, a profitable and commercially adopted strawberry harvesting system remains elusive. In this research, we explore the feasibility of using deep reinforcement learning to overcome these bottlenecks and develop a practical algorithm to address the sub-objective of viewpoint optimization, or the development of a control policy to direct a camera to favorable vantage points for autonomous harvesting. We evaluate the algorithm's performance in a custom, open-source simulated environment and observe affirmative results. Our trained …
Optimization Of Energy Harvesting Mobile Nodes Within Scalable Converter System Based On Reinforcement Learning, Chengtao Xu
Optimization Of Energy Harvesting Mobile Nodes Within Scalable Converter System Based On Reinforcement Learning, Chengtao Xu
All Graduate Theses, Dissertations, and Other Capstone Projects
Microgrid monitoring focusing on power data, such as voltage and current, has become more significant in the development of decentralized power supply system. The power data transmission delay between distributed generator is vital for evaluating the stability and financial outcome of overall grid performance. In this thesis, both hardware and simulation has been discussed for optimizing the data packets transmission delay, energy consumption, and collision rate. To minimize the transmission delay and collision rate, state-action-reward-state-action (SARSA) and Q-learning method based on Markov decision process (MDP) model is used to search the most efficient data transmission scheme for each agent device. …