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Showing 1 to 13 of 13 for “"Deep Deterministic Policy Gradient"”.
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Reinforcement Learning Control for Mobile Robot Parking with Safety Constraints
… on parking with obstacle avoidance. We apply Deep Deterministic Policy Gradient (DDPG) methods for continuous control and evaluate policies across three Simulink environments of increasing fidelity: a kinematic model, a dynamic model, and a dynamic model with actuator disturbance. In parking …
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Development and Deployment of a Dynamic Soaring Capable UAV using Reinforcement Learning
… by flying through regions of vertical wind gradient such as the wind shear layer. With reinforcement learning (RL), a fixed wing unmanned aerial vehicle (UAV) can be trained to perform DS maneuvers optimally for a variety of wind shear conditions. To accomplish this task, a 6-degreesof- …
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Cognitive GPR for subsurface sensing based on edge computing and deep reinforcement learning
… GPR based on 2D B-Scan image analysis and deep Q-learning network (DQN) is investigated. A novel entropy-based reward function is designed for the DQN model by using the results of subsurface object detection (via the region of interest identification) and recognition (via classification). …
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Designing Intelligent Energy Efficient Scheduling Algorithm To Support Massive IoT Communication In LoRa Networks
… learning-based scheduling algorithm, a Deep Deterministic policy gradient algorithm with channel activity detection (CAD) to optimize the energy efficiency of LoRaWAN in cross-layer architecture in massive IoT with star topology. We also design a CAD-based simulator for evaluating any …
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Mechanical Design and Learned Control System Development of Fiber Extrusion Device on Industrial Programmable Logic Controller (PLC) Platform.
… based on machine learning models such as DDPG (Deep Deterministic Policy Gradient). To develop and train such control algorithms, a desktop version of a fiber draw tower was designed, manufactured, and controlled via a PLC. System dynamics data was collected using a readily available preform …
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Deep Reinforcement Learning for Robotic Tasks: Manipulation and Sensor Odometry
… robotic manipulators and applied GA to Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER) (GA+DDPG+HER). Finally, we kept researching DDPG and created an algorithm named AACHER. AACHER uses HER and many independent instances of actors and critics from the …
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Comparison of Modern Controls and Reinforcement Learning for Robust Control of Autonomously Backing Up Tractor-Trailers to Loading Docks
… value. The Linear Quadratic Regulator and the Deep Deterministic Policy Gradient (DDPG) are compared for robust control when the trailer is changed. This investigation quantifies the capabilities and limitations of both controllers in simulation using a kinematic model. The controllers are …
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Resource Allocation in 5G: NR Sidelink Mode 2 & Wi-Fi 6/7
… probability. We further propose to employ Deep Deterministic Policy Gradient (DDPG) algorithm to overcome the impact of inter-vehicle collaboration in the platoon based on local information. A Monte Carlo simulator is then used to verify the analytical models' results. The numerical results …
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Using Reinforcement Learning and Bayesian Optimization on Problems in Vehicle Dynamics and Random Vibration Environmental Testing
… there is an interest in optimizing the control policy. This dissertation presents the application of nonlinear control methods to some challenging problems in vehicle automation and environmental testing.The first part of this dissertation presents the application of Reinforcement Learning (RL) …
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Deep Reinforcement Learning-Based Approaches for the MPPT Control of Standalone Solar PV Systems
… addressed by applying three model-free and off-policy deep reinforcement learning (DRL) algorithms such as Deep Deterministic Policy Gradient (DDPG), Soft Actor-Critic (SAC), and Deep Q-Network (DQN) algorithm. They are utilized mainly due to their robustness and ability to handle continuous …
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Online and offline training for adaptive neuro-fuzzy inference systems using deep and reinforcement learning with hierarchical rule-base reduction
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms
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Guidance, Navigation, and Control Techniques for Service Robotics
L'abstract è presente nell'allegato / the abstract is in the attachment
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Satellite Reorientation Using Reinforcement Learning Under Unknown Attitude Failure
… approach for reestablishing communication with deep-space satellites under unknown attitude determination and control system (ADCS) failures. When traditional fault-tolerant control methods cannot restore signal, the proposed RL controller acts as a last-resort measure by autonomously …